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Model papers from the disciplines.
Professional writers often read works by other writers to think about new techniques. Although reading a good paper cannot teach you everything you need to know about writing in a given discipline, it can be enormously helpful. The papers below are past winners of the Writing Center essay contest . We hope you find some of these helpful for expanding your writing repertoire.
It can be even more valuable to ask your professors for sample essays that they admire. In fact, if your field is not represented below, please encourage your professors to send us a model paper in that discipline.
The headings below expand to show a list of downloadable papers within the selected department or discipline.
“ Occupy Wall Street: Creating a Successful Movement from a Chaotic Structure ,” by Kelsea Jeon ’20. ENGL 114: Writing Seminar, Professor Annie Killian.
“ The Limits of Moral Ideology in Foreign HIV/AIDS Intervention ,” by Akielly Hu ’19. ENGL 114: Acting Globally, Professor Karin Gosselink.
" Treat the Problem, Not the Disease: The Necessary Shift from Vertical Programs to Horizontal Programs for Treating HIV/AIDS in sub-Saharan Africa ," by Xiuqi Cao ’17. ENGL 114: Writing Seminar, Professor Karin Gosselink.
“ Muahaha!: Defining Evil Laughter ,” by Spencer Katz ’13. ENGL 114: Writing Seminar, Professor Ryan Wepler.
“‘ The Moral Meaning of a Pause’: Ethics Committees and the Cloning Debate ,” by Lindsay Gellman ’12. ENGL 116: Writing Seminar, Professor Raymond Malewitz.
“ My Secret ,” by Lydia Martin ’12. ENGL 114: Writing Seminar, Professor Paula Resch.
“ The Camera as Dictator: Photography and Fascism at Abu Ghraib ,” by Stephanie Lynch ’09. ENGL 114: Writing Seminar, Professor Raymond Malewitz.
“ Israel’s Withdrawal from Gaza: Legitimizing Civil Disobedience from Both Sides ,” by Aya Shoshan ’10. ENGL 114: Writing Seminar, Professor Andrew Ehrgood.
“ The Curiosity of Humanity ,” by Michael Rodriguez ’10. ENGL 114: Writing Seminars 1, Professor Suzanne Young.
“ The Corrective Lens of Satire ,” by Benjamin Solarz ’09. ENGL 114: Writing Seminars I, Professor Alfred E. Guy Jr.
“ Obscuring Gender into Oneness: in Canto 21 of ‘Song of Myself ,’” by Minh Vu ’20. ENGL 127: Readings in American Literature, Professor Caleb Smith.
“ Vision, Voice, and Women in The Winter’s Tale ,” by Oriana Tang ’19. ENGL 200: Shakespeare, Comedies & Romances, Professor Catherine Nicholson, Teaching Fellow Clay Greene.
“ License to Build: Readership and Authorship in Pynchon and Melville ,” by Marc Shkurovich ’19. ENGL 127: Readings in American Literature, Professor John Durham Peters.
" ‘A Pattern of Your Love’: Sainthood as Erotic Example in ‘The Canonization’ and ‘The Relic ,’" by Eve Houghton ’17. ENGL 125: Major English Poets, Professor Benjamin Glaser.
“ How Mary Hillhouse Read Her Gray: Commonplacing the Elegy, 1768 – 1816 ,” by Eve Houghton ’17. ENGL 238: Poetry and Modernity, Restoration to Romantic, Professor Jonathan Kramnick.
“ The Governess Question: Modes of social engagement in Agnes Grey and Jane Eyre ,” by Helen Knight ’13. ENGL 431: The Brontes & Their Afterlives, Professor Linda Peterson.
“ Whither Hast Thou, Fortune, Led? ,” by Daniel Pollack ’16. ENGL 129: Tragedy, Professor Margaret Homans.
“ The Limited Potential of True Reform ,” by Bianca Yuh ’12. ENGL 117: Literature Seminars II, Professor Andrew Karas.
“ The Prophet Who Protested Too Much ,” by Sam Ayres ’09. ENGL 395: The Bible as Literature, Professor Leslie Brisman.
“ Want, Need, and Reason: Milton and Cause ,” by Annie Atura ’11. ENGL 125: Major English Poets, Professor Christopher Miller.
“ Within You, Without You: Cannibalism and Consciousness in the Transatlantic World ,” by Carina del Valle Schorske ’10. ENGL 420: Literature of the Middle Passage, Professors Shameem Black and Caryl Phillips.
“ Holiest Love: The Spiritual Valediction in ‘A Hymne to Christ ,’” by Alexandra Schwartz ’09. ENGL 125: Major English Poets, Professor George Fayen.
“ Harmony of the Flesh: The Primitivist Poetry of Disgrace ,” by Samuel Ayres ’09. ENGL 328: Fiction Without Borders, Professor Shameem Black.
“ Creation, Destruction, and Stasis in Three Poems by Shelley ,” by Noah Lawrence ’09. ENGL 249: Eng Lit & the French Revolution, Professor David Bromwich.
“ The Collapse of Difference in Stoppard’s Rosencrantz & Guildenstern Are Dead ,” by Maria Spiegel ’09. ENGL 129: The European Literary Tradition, Professor Andrea Walkden.
“ From Ass to Audience: Imagination as an Act of Translation ,” by Carina del Valle Schorske ’09. ENGL 129: The European Literary Tradition, Professor Laura Frost.
“ The Convergence of American Identity and Experience: Walt Whitman’s Concept of Democracy in ‘Song of Myself’ ,” by Alexandra Schwartz ’09. ENGL 127: Introduction to the Study of American Literature, Professor Elizabeth Dillon.
“ Love Overheard ,” by Tian Ying “Tina” Wu ’08. ENGL 125: Major English Poets, Professor Matthew Giancarlo.
“ Heart and Soul ,” by Meredith Williams ’09. ENGL 125: Major English Poets, Professor Leslie Brisman.
“ When Hell Freezes Over: Dante as Pilgrim and Poet in Inferno XXXII ,” by Lukas Cox ’19. DRST001: Literature, Professor Kathryn Slanski.
“ The essay (which others call the List) ,” by Caroline Sydney ’16. DRST 002: Directed Studies Literature, Professor Mark Bauer.
“ Paradise Lost, Again ,” by Eliana Dockterman ’13. Directed Studies: Literature, Professor Mark Bauer.
“‘ Both Soles of Every Sinner Were On Fire’: Contrapasso in Canto XI ,” by Alice Baumgartner ’10. Directed Studies: Literature, Professor Shameem Black.
“ Knocking Down the Puppet Show: Dangerous Readers in Cervantes’ Don Quixote ,” by Katy Waldman ’10. Directed Studies: Literature, Professor Richard Maxwell.
“ Sanity’s Dream: Reason and Madness, Modernity and Antiquity in King Lear and Don Quijote ,” by Joshua Tan ’09. Directed Studies: Literature, Professor Charles Hill.
" The Preserved Party: A Metonymical Still Life ," by Janine Chow ’15. LITR 202: Nabokov and World Literature, Professor Marijeta Bozovic, Teaching Fellow Daria Ezerova.
“ A-Foot and Under-Foot: Peripheries and the Footnote ,” by Catherine Reilly ’08. LITR 142: World Literature, Professor Barry McCrea.
" Formulating Maxims to Test Their Morality: Sources of Ambiguity in Kant's Moral Philosophy ," by Dan Friedman ’17. Directed Studies: Philosophy, Professor Daniel Greco.
“ Charlotte’s Finite Web: Causality in Aristotle’s Metaphysics ,” by Anya Richkind ’16. Directed Studies: Philosophy, Professor Epifanio Elizondo.
“ A Reconstruction and Critique of the Refutation of Idealism ,” by Minh Alexander Nguyen ’15. Directed Studies: Philosophy, Professor Matthew Noah Smith.
“ The Cost of Duty-Free and Duty: John Stuart Mill’s Failed Critique of Immanuel Kant, and Further Critiques of Both Philosophers ,” by Noah Lawrence ’09. Directed Studies: Philosophy, Professor Jonathan Gilmore.
“ Telling a Lie to Save a Life: Kant’s Moral Failure and Mill’s Mere Suitability ,” by Brian Earp ’10. Directed Studies: Philosophy, Professor Gregory Ganssle.
“ Self-Service ,” by Lucy McCurdy ’21. ENGL 120: Reading and Writing the Modern Essay, Professor Andrew Ehrgood.
“ Remembering the Treehouse: The Magic Between the Lines ,” by Oscar Lopez Aguirre ’20. ENGL 115: Literature Seminar, Professor Ryan Wepler.
" Laura Lee, Ink on Skin, Personal Collection of the Artist ," by Maia Hirschler ’17. ENGL 120: Reading and Writing the Modern Essay, Professor Briallen Hopper.
“ To Rufus, Who Was a Shitty Gerbil ,” by Abigail Bessler ’17. ENGL 255: Writing Humor, Professor Ryan Wepler.
" Not Today ," by Emile Greer ’15. ENGL 121: Styles of Academic and Professional Prose, Professor John Loge.
“ Why I Powerlift ,” by Chelsea Savit ’13. ENGL 120: Reading and Writing the Modern Essay, Professor Andrew Ehrgood.
“ Collecting Time ,” by Kathryn Culhane ’15. ENGL 121: Styles of Academic and Professional Prose, Professor John Loge.
“ Choice ,” by Joanna Zheng ’14. ENGL 120: Reading and Writing the Modern Essay, Professor Kim Shirkhani.
“ The Flood ,” by Michael Schulson ’12. ENGL 120: Reading and Writing the Modern Essay, Professor Barbara Stuart.
“ Choosing Terms ,” by Sarah Nutman ’11. ENGL 120: Reading and Writing the Modern Essay, Professor Richard Deming.
“ Mid-winter Walk on the Beach ,” by Kathryn Mathis ’07. ENGL 248: Nature Writing in Britain and the Colonies, Professor Linda Peterson.
“ Reindeer Bells ,” by John Thornton ’09. ENGL 120: Reading and Writing the Modern Essay, Professor William Broun.
“ The History of a Mushroom Enthusiast ,” by Sita Sunil ’19. ENGL 120: Reading and Writing the Modern Essay, Professor Kimberly Shirkhani.
“ Waking Up the Warriors: The Rise of Cancer Immunotherapy ,” by Malini Gandhi ’17. ENGL 121: Styles of Academic and Professional Prose, Professor Randi Epstein.
" Choosing to Walk the Tightrope ," by Emma Fallone ’16. ENGL 240: Writing Narrative Nonfiction, Professor Edward Ball.
" Unthinkable ," by Karen Tian ’15. ENGL 121: Styles of Academic and Professional Prose, Professor Randi Epstein.
“A Security Debriefing with R. Rosarbo on the Subject of Wilbur Cross High School ,” by Sophie Dillon ’17. ENGL 120: Reading and Writing the Modern Essay, Professor Ryan Wepler.
“ Prove It ,” by Jeremy Lent ’11. ENGL 467: Journalism, Professor Jill Abramson.
“ Round Up These Characters ,” by Presca Ahn ’10. ENGL 469: Advanced Nonfiction Writing, Professor Anne Fadiman.
“ When Culture Trumps Law ,” by Emma Sokoloff-Rubin ’11. ENGL 454: Non-Fiction Writing, Voice & Structure, Professor Fred Strebeigh.
“ Vignettes From a Carpetbagger: Four Parables of the Katrina Era ,” by Easha Anand ’08. ENGL 454: Non-Fiction Writing, Voice & Structure, Professor Fred Strebeigh.
“ 17 Genesis ,” by Isaac Selya ’08. ENGL 450: Daily Themes, Professor Bill Deresiewicz.
“ Rapha ,” by Allison Battey ’08. ENGL 454: Nonfiction, Voice and Structure, Professor Fred Strebeigh.
“ Breaking Rock ,” by Paul Gleason ’06. ENGL 469: Advanced Non-Fiction: At Home in America, Professor Anne Fadiman.
“ La Barbieria ,” by Edward Scheinman ’07. ENGL 469: Advanced Non-Fiction: At Home in America, Professor Anne Fadiman.
“ Prom King ,” by Aaron Orbey '19. ENGL 121: Cultural Critique: Style as Argument, Professor Kimberly Shirkhani.
" The Beauty of Illness ," by Jacquelyn Nakamura ’15. ENGL 121: Styles of Academic and Professional Prose, Professor Kim Shirkhani.
“ Privatization as Violence: Iraqi Oil and a Contractor Army ,” by Rosa Shapiro-Thompson ’19. HIST 042: Oil and Empire, Professor Rosie Bsheer.
" Silencing the Past by Michael-Rolph Trouillot: A Revolutionary History ," by Chentian (Lionel) Jin ’18. HIST 007: The History of U.S.-Latin American Relations, Professors Jennifer Van Vleck and Taylor Jardno.
“ Selling Dentifrice from New Delhi: Chester Bowles in India, 1951-53 ,” by Harrison Monsky ’13. HIST 134: Yale and America, Professor Jay Gitlin.
“ Silent Protection and the Burden of Silence ,” by Emma Sokoloff-Rubin ’11. HIST 160: Topics in Lesbian and Gay History, Professor George Chauncey.
“ Modern Blood Libels and the Masking of Egyptian Insecurities ,” by Matthew Bozik ’10. HIST 434: Anti-Semitism in Modern Times, Professor Paula Hyman.
“ The Progressives’ Attempts to Link America’s Rural Past and Modern Future ,” by Brooks Swett ’09. HIST 496: Nationalism in American Politics and Culture, Professor Samuel Schaffer.
“ Meanings in Canada’s Vimy Ridge Memorial ,” by Michael Birnbaum ’08. HIST 423: Cultural History of World War I, Professor Bruno Cabanes.
“ Lollard Bible Translation: Severing the Connection Between Language and Intellectual Privilege ,” by Louisa Inskip ’08. HIST 406: Medieval Heresy and Inquisition, Professor Brian Noell.
“ The Samuel and Mary Attempted Piracy Outside the Port of Cephalonia: A Case Study of Piracy Law as a Transitional Factor Away from Lex Mercatoria ,” by Michael A. Gousgounis ’06. HIST 416: Venice & The Mediterranean, 1400-1700, Professor Francesca Trivellato.
“ Entrepreneur, Democrat, Patriot: Sameness and Difference in Charles Willson Peale’s Philadelphia Museum ,” by Jordan Cutler-Tietjen ’20. HUMS 220: Collecting Nature and Art, 1500–1850, Professor Paola Bertucci, Teaching Fellow Sarah Pickman.
“ The Impossibility of P. Grad. 4 in the Thebaid and Implications for Ptolemaic Rule ,” by Jennifer Barrows ’12. CLCV 204: Alexander and the Hellenistic World, Professor Joseph Manning, Teaching Fellow Caroline Stark.
History Junior Seminar
“ Following Thread: Understanding History and Materiality in Frida Kahlo’s Clothes ,” by Deborah Monti ’19. HIST 358J: Mexico Since Independence, Professor Gilbert Joseph.
“‘ The Tories of 1812’: Decoding the Language of Political Insults in the Early Republic ,” by Zoe Rubin ’17. HIST 133J: Creation of the American Politician, Professor Joanne Freeman.
" Big Trouble in the Big Easy: The Battle of Canal Street and the Independence of Black Political Power ," by Jacob Wasserman ’16. HIST 139J: The American South Since Reconstruction, Professor Glenda Gilmore.
" Thomas Clap, Ezra Stiles, and Yale's First Revolution ," by Thomas Hopson ’16. HIST 135J: The Age of Hamilton and Jefferson, Professor Joanne Freeman.
" The Trolley Problem: The Demise of the Streetcar in New Haven ," by Jacob Wasserman ’16. HIST 116J: Roads and Cars in American Life, Professor David Spatz.
“‘ In the Fold of America’: Immigration Politics in the Alien and Sedition Era ,” by Jacob Anbinder ’14. HIST 135J: Age of Hamilton and Jefferson, Professor Joanne B. Freeman.
“ Managing History: The Federalist Attempt To Shape the Hartford Convention’s Legacy ,” by Nathaniel Zelinsky ’14. HIST 133J: The Creation of the American Politician, 1789–1820, Professor Joanne Freeman.
“ Hearts of Darkness: Opium Dens and Urban Exploration in Late Victorian London ,” by Teo Soares ’13. HIST 225J: London and Modernity, Professor Becky Conekin.
“ Mr. Madison Meets His Party: The Appointment of a Judge and The Education of a President ,” by Ryan Jacobs ’11. HIST 135J: The Age of Hamilton and Jefferson, Professor Joanne Freeman.
“ Stages of Modernity: The Thaw-Nesbit-White Scandal, the New York Press and the Drama of the Century ,” by Randall Spock ’11. HIST 126J: Murder and Mayhem in Old New York, Professor Mary Lui.
“ Dissidence in China and Eastern Europe and the Search for a New Pragmatism ,” by Eli Bildner ’10. HIST 231J: Responses to Totalitarianism, Professor Marci Shore.
“ Dancing with Knives: Voguing and Black Vernacular Dance ,” by Eliza Robertson ’18. THST 380: History of Dance, Professor Brian Seibert.
“ Having Her Pie and Eating It Too: Sara Bareilles’ Representation of Women through the Convergence of Singer-Songwriter, Stage Character, and Composer in Waitress: The Musical ,” by Sofía Campoamor ’20. MUSI 335: Women on Stage, Professors Gundula Kreuzer and Annelies Andries.
“City of Elms: The Myth of the Urban Pastoral ,” by Rebecca Ju ’21. EVST 120: American Environmental History, Professor Paul Sabin, Teaching Fellow Kelly Goodman.
“ Avoiding the sublime: Photography and the ongoing legacy of nuclear technology ,” by Colin Hemez ’18. HSAR 401: Critical Approaches to Art History, Professors Erica James and Monica Bravo.
“ Public health in the age of nuclear fallout: St. Louis and the Baby Tooth Survey 1958-1963 ,” by Kathleen Yu ’17. HSHM 448: American Medicine and the Cold War, Professor Naomi Rogers.
“ Walking With, Moving Through ,” by Holly Taylor ’17. THST 244: Writing about Movement, Professor Brian Seibert.
" From Sanctuary to Cemetery: The Fate of Astoria and the Italian Immigrant Community ," by Giovanni Bacarella ’15. AMST 348: Space, Place, & Landscape, Professor Laura Barraclough.
“‘ That’s What It Is’: Musical Potential and Stylistic Contrast in Act One, Scene One of The Most Happy Fella ,” by Dan Rubins ’16. MUSI 246: American Musical Theatre History, Professor Daniel Egan.
“ An Unattainable Salvation: Dirt, Danger & Domesticity in Old New York ,” by Catherine Carson Evans ’13. AMST 207: American Cultural Landscapes, Professor Dolores Hayden, Teaching Fellow Chloe Taft.
“ Pruitt-Igoe: Utopic Expectations Meet Tenement-Infused Realities ,” by Evan Frondorf ’14. AMST 207: American Cultural Landscapes, Professor Dolores Hayden, Teaching Fellow Liz Bondaryk.
“ The Numerous Faces of South Korea’s Burgeoning Medical Tourism Industry ,” by Lisa Wang ’12. AMST 192: Work and Daily Life in Global Capitalism, Professor Michael Denning.
“ The Prisoner Dis-Analogy as a Defense of Stem Cell Research on Spare Embryos ,” by Ilana Yurkiewicz ’10. CSDC 362: Bioethics and the Law, Professor Dov Fox.
“ Regarding the Body: The Spectacle of Dissection and Its Uses in the 18th Century ,” by Mihan Lee ’10. HSHM 431: Science/Spectacle in Enlightenment, Professor Paola Bertucci.
“ Ignoring the Call to Murder: The Evolution of Surrealist French Cinema ,” by Christopher Adler ’09. FILM 240: World Cinema, Professor Dudley Andrew.
“ The Photograph: A Personal Exploration ,” by Hannah Shearer ’09. FILM 099: Film and the Arts, Professor Dudley Andrew.
“ That Make the Strong Heart Weak ,” by Justin Jannise ’09. FILM 099: Film and the Arts, Professor Dudley Andrew.
“ Save Yourself from Yourself ,” by Ryan Hollander ’12. PLSC 114: Intro to Political Philosophy, Professor Steven Smith, Teaching Fellow Meredith Edwards.
“ Feel Like a Natural Human: The Polis by Nature, and Human Nature in Aristotle’s The Politics ,” by Laura Zax ’10. PLSC 114: Intro to Political Philosophy, Professor Steven Smith, Teaching Fellow Justin Zaremby.
“ Federal Funding for Embryonic Stem Cell Research ,” by Jurist Tan ’09. BENG 090: Stem Cells: Science & Politics, Professor Erin Lavik.
Film, Visual Arts, & Performing Arts
“ A Tale of Two States: Takeaways from Massachusetts and Louisiana in the Quest for a New Federal Education Policy ,” by Emil Friedman ’20. PLSC 214: Politics of U.S. Public Policy, Professor Jacob Hacker, Teaching Fellow Baobao Zhang.
“ Fools & Self-Representation: A Defense of Faretta v. California ,” by Daniel Cheng ’13. PLSC 252: Crime & Punishment, Professor Gregory Huber, Teaching Fellow Jeremy Kaplan-Lyman.
“ Reconsidering Broken Windows: A Critique of Moral and Pragmatic Justifications ,” by Aseem Mehta ’14. PLSC 252: Crime and Punishment, Professor Gregory Huber, Teaching Fellow Jeremy Kaplan-Lyman.
“ Pressured Justice: Activating the Courts for the Protection of Female Laborer ,” by David Wheelock ’09. PLSC 373: Comparative Judicial Politics, Professor Frances Rosenbluth, Teaching Fellow Stephen Engel.
“ Fixing Poverty: What Government Can and Cannot Do To Make America Better ,” by James Kirchick ’06. PLSC 203: Inequality and American Democracy, Professor Jacob S. Hacker, Teaching Fellow Nicole Kazee.
“ Suffering and Redemption in the Eyes of Lincoln ,” by Katerina Apostolides ’06. PLSC 314: Lincoln—Principle, Statesmanship, and Persuasion, Professors Steven Smith and David Bromwich.
Sociology, Anthropology, & Linguistics
“ The Presentation of Disability in Everyday Life ,” by Jack Lattimore ’20. SOCY 152: Topics in Contemporary Social Theory, Professor Ron Eyerman, Teaching Fellow Roger Baumann.
“ Across the Islands: Lexical and Phonetic Variation in Hawai‘ian Dialects ,” by Jackson Petty ’21. LING 112: Historical Linguistics, Professor Jonathan Manker, Teaching Fellow Martín Fuchs.
" A Diachronic Perspective on Semantic Maps ," by Robert Yaman ’15. LING 121: Historical Linguistics, Professor Claire Bowern, Teaching Fellow Sean Gleason.
" Preserving Values in a Market for Kidneys ," by Cynthia Hua ’15. SOCY 321: Sociology of Markets, Professors Devin Singh and Frederick Wherry, Teaching Fellow Andrew Cohen.
" Unweaving the ‘Development Narrative’: Bt Cotton and Farmer Suicides in India ," by Alina Aksiyote Bernardete ’16. ANTH 276: South Asian Social Worlds, Professor Sara Shneiderman.
“ Imagined Identities: The Tibetan Government-in-exile and the Western Vision of Tibet ,” by Emily Kruger ’08. ANTH 455: Religion and Globalization in East Asia, Professor Gareth Fisher.
Women’s, Gender, & Sexuality Studies
“ Congratulations, It’s a Social Construct: Production and Reproduction of (Trans) Gendered Bodies ,” by Laura Goetz ’17. WGSS 340: Feminist and Queer Theory, Professor Margaret Homans.
" Chronicles of My Life: A Minority Reading of the Dominant Narrative ," by Cathy Shen ’17. WGSS 327: Constructing Self: Autobiography, Professor Geetanjali Chanda.
“ Sex-Based Effects of Positive vs. Negative Message Framing on Intended Alcohol Use ,” by Sarah Stein ’19. PSYC 235: Research Methods in Psychology, Professor Woo-Kyoung Ahn, Teaching Fellow Natalie Wittlin.
“Effect of Excuses on Making Moral Judgments ,” by Angela Choi ’12. PSYC 235: Research Methods in Psychology, Professor Woo-Kyoung Ahn, Teaching Fellow Sarah Hailey.
“ Positive, Math-Unrelated Priming and Women’s Math Performance ,” by Jason Parad ’12. PSYC 235: Research Methods in Psychology, Professor Woo-kyoung Ahn, Teaching Fellow Jacqueline Smith.
“ Infants’ Use of Kind Information in Object Individuation and Implications for Conceptual Development ,” by Elizabeth Rawson ’07. PSYC 140: Developmental Psychology, Professor Frank Keil.
" Nutrition in Zambia ," by Christina de Fontnouvelle ’16. HLTH 230: Global Health Challenges and Responses, Professor Richard Skolnik, Teaching Fellow Jordan Emont.
" Importing Prescription Drugs from Canada: A Public Health Solution ," by Stephanie Heung ’15. PHYS 320: Science and Public Policy, Professor Bonnie Fleming.
“ Cardiovascular Disease in China ,” by Sudhakar Nuti ’13. HLTH 230: Global Health: Challenges and Responses, Professor Richard Skolnik, Teaching Fellow Nidhi Parekh.
“Neurometabolic Biomarkers for the Early Detection of Alzheimer’s Disease ,” by Ludivine Brunissen ’19. BENG 485: Fundamentals of Neuroimaging, Professors D.S. Fahmeed Hyder and Douglas L. Rothman, Teaching Fellow John J. Walsh.
“ The Construction of a Universal Entry Vector to Facilitate Genetic Modification of Rhizobia ,” by Sarah McAlister ’16. MCDB 201L: Molecular Biology Laboratory, Professor Maria Moreno.
“ Visualization of localization and expression of Arabidopsis thaliana gene AT1G52340, an ortholog of Tasselseed2 ,” by Kevin Hochstrasser ’15. MCDB 201L: Molecular Biology Laboratory, Professor Maria Moreno, Teaching Fellow Christopher Bartley.
“ Cloning of the Oryza sativa ferric chelate reductase promoter-terminator fusion into a pYU2735 plasmid: generation of a universal construct toward rice biofortification ,” by Micah Johnson ’13. MCDB 201L: Molecular Biology Laboratory, Professor Maria Moreno, Teaching Fellow Michael Turner.
“ Cloning of the Yellow Stripe 1 gene and of the promoter of a Tapetal Development and Function gene in Oryza sativa japonica ,” by Sabrina Gill ’13. MCDB 201L: Molecular Biology Laboratory, Professor Maria Moreno.
“Antimicrobial Amyloid-β: The Antagonistic Pleiotropy between Innate Immunity and Alzheimer’s Disease ,” by Emma Healy ’18. E&EB 235: Evolution and Medicine, Professor Stephen Stearns, Teaching Fellow Stephen John Gaughran.
" The Sooner, the Better: Modeling Evolutionary Recovery Following Isolated Incidents of Environmental Pollution ," by Laura Goetz ’17. BIOL 104: Ecology & Evolutionary Biology, Professor Leo Buss.
" The Influence of Egg Crypsis on the Broken-Wing Display of the Killdeer ," by Casey McLaughlin ’15. E&EB 240: Animal Behavior, Professor Suzanne Alonzo, Teaching Fellow Stacy Arnold.
“ The Hygiene Hypothesis and the Increase of Cancer in the 20th Century ,” by Stacy Scheuneman ’14. E&EB 235: Evolution and Medicine, Professor Stephen Stearns, Teaching Fellow Vanessa Lamers.
“ Research Proposal: Do Octopuses Think Like Vertebrates? A New Comparative Test ,” by Dakota E. McCoy ’13. E&EB 122: Ecology, Evolution, and Behavior, Professor Stephen Stearns, Teaching Fellow Amanda Subalusky.
“ ‘Junk’: Breeding Innovation and Complexity ,” by Jared Shenson ’12. E&EB 122: Principles of Evolution, Ecology and Behavior. Professor Steven Stearns, Teaching Fellow Andrea Hodgins-Davis.
“ Evaluating the influence of evolution on human brain size ,” by Sarah Foote ’10. E&EB 122: Principles of Evolution, Ecology and Behavior. Professor Steven Stearns, Teaching Fellow Katy Richards-Hrdlicka.
“ Fly Sex: Adaptive manipulation of offspring sex ratio in Drosophila melanogaster ,” by Tse Yang Lim ’11. E&EB 240: Animal Behavior, Professor Suzanne Alonzo, Teaching Fellow Andrea Hodgins-Davis.
“ Recombination in Mitochondrial DNA: Nonzero but Rare ,” by Christina Hueschen ’10. E&EB 122: Principles of Evolution, Ecology and Behavior, Professor Stephen Stearns, Teaching Fellow Jeremy Draghi.
“ Reconstructing Calamites: Building Giants from Fragments ,” by Alena Gribskov ’09. E&EB 171: Collections of the Peabody Museum, Professor Leo Buss.
" Electrospray Synthesis of Graphene Oxide-Mized Metal Oxide Nanocomposites for Energy Storage ," by Brandon Ortiz ’18. STARS, Professor Alessandro Gomez, Teaching Fellow Justin Tang.
“ Determining the Ages, Metallicities, and Star Formation Rates of Brightest Cluster Galaxies ,” by Hannah Alpert ’15. SCIE S101: Scientific Research: Process and Presentation, Professor Maria Mareno.
“ An Introduction to Nuclear Magnetic Resonance Spectroscopy ,” by Andrew Yang ’12. CHEM 251L: Inorganic Chemistry Laboratory, Professor Jonathan Parr.
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- Open Access
- Published: 19 October 2019
Smart literature review: a practical topic modelling approach to exploratory literature review
- Claus Boye Asmussen ORCID: orcid.org/0000-0002-2998-2293 1 &
- Charles Møller 1
Journal of Big Data volume 6 , Article number: 93 ( 2019 ) Cite this article
Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.
Manual exploratory literature reviews are soon to be outdated. It is a time-consuming process, with limited processing power, resulting in a low number of papers analysed. Researchers, especially junior researchers, often need to find, organise, and understand new and unchartered research areas. As a literature review in the early stages often involves a large number of papers, the options for a researcher is either to limit the amount of papers to review a priori or review the papers by other methods. So far, the handling of large collections of papers has been structured into topics or categories by the use of coding sheets [ 2 , 12 , 22 ], dictionary or supervised learning methods [ 30 ]. The use of coding sheets has especially been used in social science, where trained humans have created impressive data collections, such as the Policy Agendas Project and the Congressional Bills Project in American politics [ 30 ]. These methods, however, have a high upfront cost of time, requiring a prior understanding where papers are grouped by categories based on pre-existing knowledge. In an exploratory phase where a general overview of research directions is needed, many researchers may be dismayed by having to spend a lot of time before seeing any results, potentially wasting efforts that could have been better spent elsewhere. With the advancement of machine learning methods, many of the issues can be dealt with at a low cost of time for the researcher. Some authors argue that when human processing such as coding practice is substituted by computer processing, reliability is increased and cost of time is reduced [ 12 , 23 , 30 ]. Supervised learning and unsupervised learning, are two methods for automatically processing papers [ 30 ]. Supervised learning relies on manually coding a training set of papers before performing an analysis, which entails a high cost of time before a result is achieved. Unsupervised learning methods, such as topic modelling, do not require the researcher to create coding sheets before an analysis, which presents a low cost of time approach for an exploratory review with a large collection of papers. Even though, topic modelling has been used to group large amounts of documents, few applications of topic modelling have been used on research papers, and a researcher is required to have programming skills and statistical knowledge to successfully conduct an exploratory literature review using topic modelling.
This paper presents a framework where topic modelling, a branch of the unsupervised methods, is used to conduct an exploratory literature review and how that can be used for a full literature review. The intention of the paper is to enable the use of topic modelling for researchers by providing a practical approach to topic modelling, where a framework is presented and used on a case step-by-step. The paper is organised as follows. The following section will review the literature in topic modelling and its use in exploratory literature reviews. The framework is presented in “ Method ” section, and the case is presented in “ Framework ” section. “ Discussion ” and “ Conclusion ” sections conclude the paper with a discussion and conclusion.
Topic modelling for exploratory literature review
While there are many ways of conducting an exploratory review, most methods require a high upfront cost of time and having pre-existent knowledge of the domain. Quinn et al. [ 30 ] investigated the costs of different text categorisation methods, a summary of which is presented in Table 1 , where the assumptions and cost of the methods are compared.
What is striking is that all of the methods, except manually reading papers and topic modelling, require pre-existing knowledge of the categories of the papers and have a high pre-analysis cost. Manually reading a large amount of papers will have a high cost of time for the researcher, whereas topic modelling can be automated, substituting the use of the researcher’s time with the use of computer time. This indicates a potentially good fit for the use of topic modelling for exploratory literature reviews.
The use of topic modelling is not new. However, there are remarkably few papers utilising the method for categorising research papers. It has been predominantly been used in the social sciences to identify concepts and subjects within a corpus of documents. An overview of applications of topic modelling is presented in Table 2 , where the type of data, topic modelling method, the use case and size of data are presented.
The papers in Table 2 analyse web content, newspaper articles, books, speeches, and, in one instance, videos, but none of the papers have applied a topic modelling method on a corpus of research papers. However, [ 27 ] address the use of LDA for researchers and argue that there are four parameters a researcher needs to deal with, namely pre-processing of text, selection of model parameters and number of topics to be generated, evaluation of reliability, and evaluation of validity. The uses of topic modelling are to identify themes or topics within a corpus of many documents, or to develop or test topic modelling methods. The motivation for most of the papers is that the use of topic modelling enables the possibility to do an analysis on a large amount of documents, as they would otherwise have not been able to due to the cost of time [ 30 ]. Most of the papers argue that LDA is a state-of-the-art and preferred method for topic modelling, which is why almost all of the papers have chosen the LDA method. The use of topic modelling does not provide a full meaning of the text but provides a good overview of the themes, which could not have been obtained otherwise [ 21 ]. DiMaggio et al. [ 12 ] find a key distinction in the use of topic modelling is that its use is more of utility than accuracy, where the model should simplify the data in an interpretable and valid way to be used for further analysis They note that a subject-matter expert is required to interpret the outcome and that the analysis is formed by the data.
The use of topic modelling presents an opportunity for researchers to add a tool to their tool box for an exploratory and literature review process. Topic modelling has mostly been used on online content and requires a high degree of statistical and technical skill, skills not all researchers possess. To enable more researchers to apply topic modelling for their exploratory literature reviews, a framework will be proposed to lower the requirements for technical and statistical skills of the researcher.
Topic modelling has proven itself as a tool for exploratory analysis of a large number of papers [ 14 , 24 ]. However, it has rarely been applied in the context of an exploratory literature review. The selected topic modelling method, for the framework, is Latent Dirichlet Allocation (LDA), as it is the most used [ 6 , 12 , 17 , 20 , 32 ], state-of-the-art method [ 25 ] and simplest method [ 8 ]. While other topic modelling methods could be considered, the aim of this paper is to enable the use of topic modelling for researchers. For enabling topic modelling for researchers, ease of use and applicability are highly rated, where LDA is easily implemented and understood. Other topic modelling methods could potentially be used in the framework, where reviews of other topic models is presented in [ 1 , 26 ].
The topic modelling method LDA is an unsupervised, probabilistic modelling method which extracts topics from a collection of papers. A topic is defined as a distribution over a fixed vocabulary. LDA analyses the words in each paper and calculates the joint probability distribution between the observed (words in the paper) and the unobserved (the hidden structure of topics). The method uses a ‘Bag of Words’ approach where the semantics and meaning of sentences are not evaluated. Rather, the method evaluates the frequency of words. It is therefore assumed that the most frequent words within a topic will present an aboutness of the topic. As an example, if one of the topics in a paper is LEAN, then it can be assumed that the words LEAN, JIT and Kanban are more frequent, compared to other non-LEAN papers. The result is a number of topics with the most prevalent topics grouped together. A probability for each paper is calculated for each topic, creating a matrix with the size of number of topics multiplied with the number of papers. A detailed description of LDA is found in [ 6 ].
The framework is designed as a step-by-step procedure, where its use is presented in a form of a case where the code used for the analysis is shared, enabling other researchers to easily replicate the framework for their own literature review. The code is based on the open source statistical language R, but any language with the LDA method is suitable for use. The framework can be made fully automated, presenting a low cost of time approach for exploratory literature reviews. An inspiration for the automation of the framework can be found in [ 10 ], who created an online-service, towards processing Business Process Management documents where text-mining approaches such as topic modelling are automated. They find that topic modelling can be automated and argue that the use of a good tool for topic modelling can easily present good results, but the method relies on the ability of people to find the right data, guide the analytical journey and interpret the results.
The aim of the paper is to create a generic framework which can be applied in any context of an exploratory literature review and potentially be used for a full literature review. The method provided in this paper is a framework which is based upon well-known procedures for how to clean and process data, in such a way that the contribution from the framework is not in presenting new ways to process data but in how known methods are combined and used. The framework will be validated by the use of a case in the form of a literature review. The outcome of the method is a list of topics where papers are grouped. If the grouping of papers makes sense and is logical, which can be evaluated by an expert within the research field, then the framework is deemed valid. Compared to other methods, such as supervised learning, the method of measuring validity does not produce an exact degree of validity. However, invalid results will likely be easily identifiable by an expert within the field. As stated by [ 12 ], the use of topic modelling is more for utility than for accuracy.
The developed framework is illustrated in Fig. 1 , and the R-code and case output files are located at https://github.com/clausba/Smart-Literature-Review . The smart literature review process consists of the three steps: pre-processing, topic modelling, and post-processing.
Process overview of the smart literature review framework
The pre-processing steps are getting the data and model ready to run, where the topic-modelling step is executing the LDA method. The post-processing steps are translating the outcome of the LDA model to an exploratory review and using that to identify papers to be used for a literature review. It is assumed that the papers for review are downloaded and available, as a library with the pdf files.
The pre-processing steps consist of loading and preparing the papers for processing, an essential step for a good analytical result. The first step is to load the papers into the R environment. The next step is to clean the papers by removing or altering non-value-adding words. All words are converted to lower case, and punctuation and whitespaces are removed. Special characters, URLs, and emails are removed, as they often do not contribute to identification of topics. Stop words, misread words and other non-semantic contributing words are removed. Examples of stop words are “can”, “use”, and “make”. These words add no value to the aboutness of a topic. The loading of papers into R can in some instances cause words to be misread, which must either be rectified or removed. Further, some websites add a first page with general information, and these contain words that must be removed. This prevents unwanted correlation between papers downloaded from the same source. Words are stemmed to their root form for easier comparison. Lastly, many words only occur in a single paper, and these should be removed to make computations easier, as less frequent words will likely provide little benefit in grouping papers into topics.
The cleansing process is often an iterative process, as it can be difficult to identify all misread and non-value adding-words a priori. Different papers’ corpora contain different words, which means that an identical cleaning process cannot be guaranteed if a new exploratory review is conducted. As an example, different non-value-adding words exist for the medical field compared to sociology or supply chain management (SCM). The cleaning process is finished once the loaded papers mainly contain value-adding words. There is no known way to scientifically evaluate when the cleaning process is finished, which in some instances makes the cleaning process more of an art than science. However, if a researcher is technically inclined methods, provided in the preText R-package can aid in making a better cleaning process [ 11 ].
LDA is an unsupervised method, which means we do not, prior to the model being executed, know the relationship between the papers. A key aspect of LDA is to group papers into a fixed number of topics, which must be given as a parameter when executing LDA. A key process is therefore to estimate the optimal number of topics. To estimate the number of topics, a cross-validation method is used to calculate the perplexity, as used in information theory, and it is a metric used to evaluate language models, where a low score indicates a better generalisation model, as done by [ 7 , 31 , 32 ]. Lowering the perplexity score is identical to maximising the overall probability of papers being in a topic. Next, test and training datasets are created: the LDA algorithm is run on the training set, and the test set is used to validate the results. The criteria for selecting the right number of topics is to find the balance between a useable number of topics and, at the same time, to keep the perplexity as low as possible. The right number of topics can differ greatly, depending on the aim of the analysis. As a rule of thumb, a low number of topics is used for a general overview and a higher number of topics is used for a more detailed view.
The cross-validation step is used to make sure that a result from an analysis is reliable, by running the LDA method several times under different conditions. Most of the parameters set for the cross-validation should have the same value, as in the final topic modelling run. However, due to computational reasons, some parameters can be altered to lower the amount of computation to save time. As with the number of topics, there is no right way to set the parameters, indicating a trial-and-error process. Most of the LDA implementations have default values set, but in this paper’s case the following parameters were changed: burn-in time, number of iterations, seed values, number of folds, and distribution between training and test sets.
- Topic modelling
Once the papers have been cleaned and a decision has been made on the number of topics, the LDA method can be run. The same parameters as used in the cross-validation should be used as a guidance but for more precise results, parameters can be changed such as a higher number of iterations. The number of folds should be removed, as we do not need a test set, as all papers will be used to run the model. The outcome of the model is a list of papers, a list of probabilities for each paper for each topic, and a list of the most frequent words for each topic.
If an update to the analysis is needed, new papers simply have to be loaded and the post-processing and topic modelling steps can be re-run without any alterations to the parameters. Thus, the framework enables an easy path for updating an exploratory review.
The aim of the post-processing steps is to identify and label research topics and topics relevant for use in a literature review. An outcome of the LDA model is a list of topic probabilities for each paper. The list is used to assign a paper to a topic by sorting the list by highest probability for each paper for each topic. By assigning the papers to the topics with the highest probability, all of the topics contain papers that are similar to each other. When all of the papers have been distributed into their selected topics, the topics need to be labelled. The labelling of the topics is found by identifying the main topic of each topic group, as done in [ 17 ]. Naturally, this is a subjective matter, which can provide different labelling of topics depending on the researcher. To lower the risk of wrongly identified topics, a combination of reviewing the most frequent words for each topic and a title review is used. After the topics have been labelled, the exploratory search is finished.
When the exploratory search has finished, the results must be validated. There are three ways to validate the results of an LDA model, namely statistical, semantic, or predictive [ 12 ]. Statistical validation uses statistical methods to test the assumptions of the model. An example is [ 28 ], where a Bayesian approach is used to estimate the fit of papers to topics. Semantic validation is used to compare the results of the LDA method with expert reasoning, where the results must make semantic sense. In other words, does the grouping of papers into a topic make sense, which ideally should be evaluated by an expert. An example is [ 18 ], who utilises hand coding of papers and compare the coding of papers to the outcome of an LDA model. Predictive validation is used if an external incident can be correlated with an event not found in the papers. An example is in politics where external events, such as presidential elections which should have an impact on e.g. press releases or newspaper coverage, can be used to create a predictive model [ 12 , 17 ].
The chosen method for validation in this framework is semantic validation. The reason is that a researcher will often be or have access to an expert who can quickly validate if the grouping of papers into topics makes sense or not. Statistical validation is a good way to validate the results. However, it would require high statistical skills from the researchers, which cannot be assumed. Predictive validation is used in cases where external events can be used to predict the outcome of the model, which is seldom the case in an exploratory literature review.
It should be noted that, in contrast to many other machine learning methods, it is not possible to calculate a specific measure such as the F-measure or RMSE. To be able to calculate such measures, there must exist a correct grouping of papers, which in this instance would often mean comparing the results to manually created coding sheets [ 11 , 19 , 20 , 30 ]. However, it is very rare that coding sheets are available, leaving the semantic validation approach as the preferred validation method. The validation process in the proposed framework is two-fold. Firstly, the title of the individual paper must be reviewed to validate that each paper does indeed belong in its respective topic. As LDA is an unsupervised method, it can be assumed that not all papers will have a perfect fit within each topic, but if the majority of papers are within the theme of the topic, it is evaluated to be a valid result. If the objective of the research is only an exploratory literature review, the validation ends here. However, if a full literature review is conducted, the literature review can be viewed as an extended semantic validation method. By reviewing the papers in detail within the selected topics of research, it can be validated if the vast majority of papers belong together.
Using the results from the exploratory literature review for a full literature review is simple, as all topics from the exploratory literature review will be labelled. To conduct the full literature review, select the relevant topics and conduct the literature review on the selected papers.
To validate the framework, a case will be presented, where the framework is used to conduct a literature review. The literature review is conducted in the intersection of the research fields analytics, SCM, and enterprise information systems [ 3 ]. As the research areas have a rapidly growing interest, it was assumed that the number of papers would be large, and that an exploratory review was needed to identify the research directions within the research fields. The case used broadly defined keywords for searching for papers, ensuring to include as many potentially relevant papers as possible. Six hundred and fifty papers were found, which were heavily reduced by the use of the smart literature review framework to 76 papers, resulting in a successful literature review. The amount of papers is evaluated to be too time-consuming for a manual exploratory review, which provides a good case to test the smart literature review framework. The steps and thoughts behind the use of the framework are presented in this case section.
The first step was to load the 650 papers into the R environment. Next, all words were converted to lowercase and punctuation, whitespaces, email addresses, and URLs were removed. Problematic words were identified, such as words incorrectly read from the papers. Words included in a publisher’s information page were removed, as they add no semantic value to the topic of a paper. English stop words were removed, and all words were stemmed. As a part of an iterative process, several papers were investigated to evaluate the progress of cleaning the papers. The investigations were done by displaying words in a console window and manually evaluating if more cleaning had to be done.
After the cleaning steps, 256,747 unique words remained in the paper corpus. This is a large number of unique words, which for computational reasons is beneficial to reduce. Therefore, all words that did not have a sparsity or likelihood of 99% to be in any paper were removed. The operation lowered the amount of unique words to 14,145, greatly reducing the computational needs. The LDA method will be applied on the basis of the 14,145 unique words for the 650 papers. Several papers were manually reviewed, and it was evaluated that removal of the unique words did not significantly worsen the ability to identify main topics of the paper corpus.
The last step of pre-processing is to identify the optimal number of topics. To approximate the optimal number of topics, two things were considered. The perplexity was calculated for different amounts of topics, and secondly the need for specificity was considered.
At the extremes, choosing one topic would indicate one topic covering all papers, which will provide a very coarse view of the papers. On the other hand, if the number of topics is equal to the number of papers, then a very precise topic description will be achieved, although the topics will lose practical use as the overview of topics will be too complex. Therefore, a low number of topics was preferred as a general overview was required. Identifying what is a low number of topics will differ depending on the corpus of papers, but visualising the perplexity can often provide the necessary aid for the decision.
The perplexity was calculated over five folds, where each fold would identify 75% of the papers for training the model and leave out the remaining 25% for testing purposes. Using multiple folds reduces the variability of the model, ensuring higher reliability and reducing the risk of overfitting. For replicability purposes, specific seed values were set. Lastly, the number of topics to evaluate is selected. In this case, the following amounts of topics were selected: 2, 3, 4, 5, 10, 20, 30, 40, 50, 75, 100, and 200. The perplexity method in the ‘topicmodels’ R library is used, where the specific parameters can be found in the provided code.
The calculations were done over two runs. However, there is no practical reason for not running the calculations in one run. The first run included all values of number of topics below 100, and the second run calculated the perplexity for 100 and 200 number of topics. The runtimes for the calculations were respectively 9 and 10 h on a standard issue laptop. The combined results are presented in Fig. 2 , and the converged results can be found in the shared repository.
5-Fold cross-validation of topic modelling. Results of cross-validation
The goal in this case is to find the lowest number of topics, which at the same time have a low perplexity. In this case, the slope of the fitted line starts to gradually decline at twenty topics, which is why the selected number of topics is twenty.
Case: topic modelling
As the number of topics is chosen, the next step is to run the LDA method on the entire set of papers. The full run of 650 papers for 20 topics took 3.5 h to compute on a standard issue laptop. An outcome of the method is a 650 by 20 matrix of topic probabilities. In this case, the papers with the highest probability for each topic were used to allocate the papers. The allocation of papers to topics was done in Microsoft Excel. An example of how a distribution of probabilities is distributed across topics for a specific paper is depicted in Fig. 3 . Some papers have topic probability values close to each other, which could indicate a paper belonging to an intersection between two or more topics. These cases were not considered, and the topic with the highest probability was selected.
Example of probability distribution for one document (Topic 16 selected)
The allocation of papers to topics resulted in the distribution depicted in Fig. 4 . As can be seen, the number of papers varies for each topic, indicating that some research areas have more publications than others do.
Distribution of papers per topic
Next step is to process the findings and find an adequate description of the topics. A combination of reviewing the most frequent words and a title review was used to identify the topic names. Practically, all of the paper titles and the most frequent words for each topic, were transferred to a separate Excel spreadsheet, providing an easy overview of paper titles. An example for topic 17 can be seen in Table 3 . The most frequent words for the papers in topic 17 are “data”, “big” and “analyt”. Many of the paper titles also indicate usage of big data and analytics for application in a business setting. The topic is named “Big Data Analytics”.
The process was repeated for all other topics. The names of the topics are presented in Tables 4 and 5 .
Based on the names of the topics, three topics were selected based on relevancy for the literature review. Topics 5, 13, and 17 were selected, with a total of 99 papers. In this specific case, it was deemed that there might be papers with a sub-topic that is not relevant for the literature review. Therefore, an abstract review was conducted for the 99 papers, creating 10 sub-topics, which are presented in Table 6 .
The sub-topics RFID, Analytical Methods, Performance Management, and Evaluation and Selection of IT Systems were evaluated to not be relevant for the literature review. Seventy-six papers remained, grouped by sub-topics.
The outcome of the case was an overview of the research areas within the paper corpus, represented by the twenty topics and the ten sub-topics. The selected sub-topics were used to conduct a literature review. The validation of the framework consisted of two parts. The first part addressed the question of whether the grouping of papers, evaluated by the title and keywords, makes sense and the second part addressed whether the literature review revealed any misplaced papers. The framework did successfully place the selected papers into groups of papers that resemble each other. There was only one case where a paper was misplaced, namely that a paper about material informatics was placed among the papers in the sub-topic EIS and Analytics. The grouping and selection of papers in the literature review, based on the framework, did make semantic sense and was successfully used for a literature review. The framework has proven its utility in enabling a faster and more comprehensive exploratory literature review, as compared to competing methods. The framework has increased the speed for analysing a large amount of papers, as well as having increased the reliability in comparison with manual reviews as the same result can be obtained by running the analysis once again. The transparency in the framework is higher than in competing methods, as all steps of the framework are recorded in the code and output files.
This paper presents an approach not often found in academia, by using machine learning to explore papers to identify research directions. Even though the framework has its limitations, the results and ease of use leave a promising future for topic-modelling-based exploratory literature reviews.
The main benefit of the framework is that it provides information about a large number of papers, with little effort on the researcher’s part, before time-costly manual work is to be done. It is possible, by the use of the framework, to quickly navigate many different paper corpora and evaluate where the researchers’ time and focus should be spent. This is especially valuable for a junior researcher or a researcher with little prior knowledge of a research field. If default parameters and cleaning settings can be found for the steps in the framework, a fully automatic grouping of papers could be enabled, where very little work has to be done to achieve an overview of research directions. From a literature review perspective, the benefit of using the framework is that the decision to include or exclude papers for a literature review will be postponed to a later stage where more information is provided, resulting in a more informed decision-making process. The framework enables reproducibility, as all of the steps in the exploratory review process can be reproduced, and enables a higher degree of transparency than competing methods do, as the entire review process can, in detail, be evaluated by other researchers.
There is practically no limit of the number of papers the framework is able to process, which could enable new practices for exploratory literature reviews. An example is to use the framework to track the development of a research field, by running the topic modelling script frequently or when new papers are published. This is especially potent if new papers are automatically downloaded, enabling a fully automatic exploratory literature review. For example, if an exploratory review was conducted once, the review could be updated constantly whenever new publications are made, grouping the publications into the related topics. For this, the topic model has to be trained properly for the selected collection of papers, where it can be assumed that minor additions of papers would likely not warrant any changes to the selected parameters of the model. However, as time passes and more papers are processed, the model will learn more about the collection of papers and provide a more accurate and updated result. Having an automated process could also enable a faster and more reliable method to do post-processing of the results, reducing the post-analysis cost identified for topic modelling by [ 30 ], from moderate to low.
The framework is designed to be easily used by other researchers by designing the framework to require less technical knowledge than a normal topic model usage would entail and by sharing the code used in the case work. The framework is designed as a step-by-step approach, which makes the framework more approachable. However, the framework has yet not been used by other researchers, which would provide valuable lessons for evaluating if the learning curve needs to be lowered even further for researchers to successfully use the framework.
There are, however, considerations that must be addressed when using the smart literature review framework. Finding the optimal number of topics can be quite difficult, and the proposed method of cross-validation based on the perplexity presented a good, but not optimal, solution. An indication of why the number of selected topics is not optimal is the fact that it was not possible to identify a unifying topic label for two of the topics. Namely topics 12 and 20, which were both labelled miscellaneous. The current solution to this issue is to evaluate the relevancy of every single paper of the topics that cannot be labelled. However, in future iterations of the framework, a better identification of the number of topics must be developed. This is a notion also recognised by [ 6 ], who requested that researchers should find a way to label and assign papers to a topic other than identifying the most frequent words. An attempt was made by [ 17 ] to generate automatic labelling on press releases, but it is uncertain if the method will work in other instances. Overall, the grouping of papers in the presented case into topics generally made semantic sense, where a topic label could be found for the majority of topics.
A consideration when using the framework is that not all steps have been clearly defined, and, e.g., the cleaning step is more of an art than science. If a researcher has no or little experience in coding or executing analytical models, suboptimal results could occur. [ 11 , 25 , 27 ] find that especially the pre-processing steps can have a great impact on the validity of results, which further emphasises the importance of selecting model parameters. However, it is found that the default parameters and cleaning steps set in the code provided a sufficiently valid and useable result for an exploratory literature analysis. Running the code will not take much of the researcher’s time, as the execution of code is mainly machine time, and verifying the results takes a limited amount of a researcher time.
Due to the semantic validation method used in the framework, it relies on the availability of a domain expert. The domain expert will not only validate if the grouping of papers into topics makes sense, but it is also their responsibility to label the topics [ 12 ]. If a domain expert is not available, it could lead to wrongly labelled topics and a non-valid result.
A key issue with topic modelling is that a paper can be placed in several related topics, depending on the selected seed value. The seed value will change the starting point of the topic modelling, which could result in another grouping of papers. A paper consists of several sub-topics and depending on how the different sub-topics are evaluated, papers can be allocated to different topics. A way to deal with this issue is to investigate papers with topic probabilities close to each other. Potential wrongly assigned papers can be identified and manually moved if deemed necessary. However, this presents a less automatic way of processing the papers, where future research should aim to improve the assignments of papers to topics or create a method to provide an overview of potentially misplaced papers. It should be noted that even though some papers can be misplaced, the framework provides outcome files than can easily be viewed to identify misplaced papers, by a manual review.
As the smart literature review framework heavily relies on topic modelling, improvements to the selected topic model will likely present better results. The results of the LDA method have provided good results, but more accurate results could be achieved if the semantic meaning of the words would be considered. The framework has only been tested on academic papers, but there is no technical reason to not include other types of documents. An example is to use the framework in a business context to analyse meeting minutes notes to analyse the discussion within the different departments in a company. For this to work, the cleaning parameters would likely have to change, and another evaluation method other than a literature review would be applicable. Further, the applicability of the framework has to be assessed on other streams of literature to be certain of its use for exploratory literature reviews at large.
This paper aimed to create a framework to enable researchers to use topic modelling to, do an exploratory literature review, decreasing the need for manually reading papers and, enabling the possibility to analyse a greater, almost unlimited, amount of papers, faster, more transparently and with greater reliability. The framework is based upon the use of the topic model Latent Dirichlet Allocation, which groups related papers into topic groups. The framework provides greater reliability than competing exploratory review methods provide, as the code can be rerun on the same papers, which will provide identical results. The process is highly transparent, as most decisions made by the researcher can be reviewed by other researchers, unlike, e.g., in the creation of coding sheets. The framework consists of three main phases: Pre-processing, Topic Modelling, and Post-Processing. In the pre-processing stage, papers are loaded, cleaned, and cross-validated, where recommendations to parameter settings are provided in the case work, as well as in the accompanied code. The topic modelling step is where the LDA method is executed, using the parameters identified in the pre-processing step. The post-processing step creates outputs from the topic model and addresses how validity can be ensured and how the exploratory literature review can be used for a full literature review. The framework was successfully used in a case with 650 papers, which was processed quickly, with little time investment from the researcher. Less than 2 days was used to process the 650 papers and group them into twenty research areas, with the use of a standard laptop. The results of the case are used in the literature review by [ 3 ].
The framework is seen to be especially relevant for junior researchers, as they often need an overview of different research fields, with little pre-existing knowledge, where the framework can enable researchers to review more papers, more frequently.
For an improved framework, two main areas need to be addressed. Firstly, the proposed framework needs to be applied by other researchers on other research fields to gain knowledge about the practicality and gain ideas for further development of the framework. Secondly, research in how to automatically identity model parameters could greatly improve the usability for the use of topic modelling for non-technical researchers, as the selection of model parameters has a great impact on the result of the framework.
Availability of data and materials
https://github.com/clausba/Smart-Literature-Review (No data).
- Latent Dirichlet Allocation
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Asmussen, C.B., Møller, C. Smart literature review: a practical topic modelling approach to exploratory literature review. J Big Data 6 , 93 (2019). https://doi.org/10.1186/s40537-019-0255-7
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Published : 19 October 2019
DOI : https://doi.org/10.1186/s40537-019-0255-7
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- How to Write a Literature Review | Guide, Examples, & Templates
How to Write a Literature Review | Guide, Examples, & Templates
Published on January 2, 2023 by Shona McCombes . Revised on August 15, 2023.
What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .
There are five key steps to writing a literature review:
- Search for relevant literature
- Evaluate sources
- Identify themes, debates, and gaps
- Outline the structure
- Write your literature review
A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.
Table of contents
What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.
- Quick Run-through
- Step 1 & 2
When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:
- Demonstrate your familiarity with the topic and its scholarly context
- Develop a theoretical framework and methodology for your research
- Position your work in relation to other researchers and theorists
- Show how your research addresses a gap or contributes to a debate
- Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.
Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.
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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.
- Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
- Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
- Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
- Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)
You can also check out our templates with literature review examples and sample outlines at the links below.
Download Word doc Download Google doc
Before you begin searching for literature, you need a clearly defined topic .
If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .
Make a list of keywords
Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.
- Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
- Body image, self-perception, self-esteem, mental health
- Generation Z, teenagers, adolescents, youth
Search for relevant sources
Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:
- Your university’s library catalogue
- Google Scholar
- Project Muse (humanities and social sciences)
- Medline (life sciences and biomedicine)
- EconLit (economics)
- Inspec (physics, engineering and computer science)
You can also use boolean operators to help narrow down your search.
Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.
You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.
For each publication, ask yourself:
- What question or problem is the author addressing?
- What are the key concepts and how are they defined?
- What are the key theories, models, and methods?
- Does the research use established frameworks or take an innovative approach?
- What are the results and conclusions of the study?
- How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
- What are the strengths and weaknesses of the research?
Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.
You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.
Take notes and cite your sources
As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.
It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.
Receive feedback on language, structure, and formatting
Professional editors proofread and edit your paper by focusing on:
- Academic style
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See an example
To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:
- Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
- Themes: what questions or concepts recur across the literature?
- Debates, conflicts and contradictions: where do sources disagree?
- Pivotal publications: are there any influential theories or studies that changed the direction of the field?
- Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?
This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.
- Most research has focused on young women.
- There is an increasing interest in the visual aspects of social media.
- But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.
There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).
The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.
Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.
If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.
For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.
If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:
- Look at what results have emerged in qualitative versus quantitative research
- Discuss how the topic has been approached by empirical versus theoretical scholarship
- Divide the literature into sociological, historical, and cultural sources
A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.
You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.
Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.
The introduction should clearly establish the focus and purpose of the literature review.
Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.
As you write, you can follow these tips:
- Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
- Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
- Critically evaluate: mention the strengths and weaknesses of your sources
- Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts
In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.
When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !
This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.
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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
- Sampling methods
- Simple random sampling
- Stratified sampling
- Cluster sampling
- Likert scales
- Null hypothesis
- Statistical power
- Probability distribution
- Effect size
- Poisson distribution
- Optimism bias
- Cognitive bias
- Implicit bias
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- Explicit bias
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .
It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.
There are several reasons to conduct a literature review at the beginning of a research project:
- To familiarize yourself with the current state of knowledge on your topic
- To ensure that you’re not just repeating what others have already done
- To identify gaps in knowledge and unresolved problems that your research can address
- To develop your theoretical framework and methodology
- To provide an overview of the key findings and debates on the topic
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .
A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .
An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .
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