How We Do Things With Words: Analyzing Text as Social and Cultural Data
暂无分享,去创建一个
Maria Liakata | Jacob Eisenstein | Simon DeDeo | Jane Winters | Rebekah Tromble | Dong Nguyen | David Mimno | Jacob Eisenstein | Maria Liakata | David Mimno | D. Nguyen | Rebekah Tromble | S. Dedeo | J. Winters
[1] Benedikt Szmrecsanyi,et al. A statistical method for the identification and aggregation of regional linguistic variation , 2011 .
[2] Chris Welty,et al. Crowd Truth: Harnessing disagreement in crowdsourcing a relation extraction gold standard , 2013 .
[3] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.
[4] Corina Koolen,et al. These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution , 2017, EthNLP@EACL.
[5] Daniel Jurafsky,et al. How to Ask for a Favor: A Case Study on the Success of Altruistic Requests , 2014, ICWSM.
[6] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[7] Barbara McGillivray,et al. Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings , 2019, EMNLP.
[8] Graeme Hirst,et al. A Tale of Two Cultures: Bringing Literary Analysis and Computational Linguistics Together , 2013, CLfL@NAACL-HLT.
[9] Shion Guha,et al. Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence? , 2017, J. Assoc. Inf. Sci. Technol..
[10] David Bamman,et al. A Bayesian Mixed Effects Model of Literary Character , 2014, ACL.
[11] Cristian Danescu-Niculescu-Mizil,et al. Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions , 2016, WWW.
[12] Erik Bleich,et al. The effect of terrorist events on media portrayals of Islam and Muslims: evidence from New York Times headlines, 1985–2013 , 2016 .
[13] Kenneth Benoit,et al. Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark , 2012, Political Analysis.
[14] David Bamman,et al. Gender identity and lexical variation in social media , 2012, 1210.4567.
[15] William R. Frey,et al. Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data , 2018, Social science computer review.
[16] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[17] Andrew Piper,et al. Think Small: On Literary Modeling , 2017, PMLA/Publications of the Modern Language Association of America.
[18] Jennifer L Stevens Aubrey,et al. Looking Good Versus Feeling Good: An Investigation of Media Frames of Health Advice and Their Effects on Women’s Body-related Self-perceptions , 2010 .
[19] Erik Bleich,et al. Media Portrayals of Minorities: Muslims in British Newspaper Headlines, 2001–2012 , 2015 .
[20] Jacob Eisenstein,et al. What to do about bad language on the internet , 2013, NAACL.
[21] Alexander Binder,et al. Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.
[22] Matthew J. Salganik,et al. Bit by bit: social research in the digital age , 2019, The Journal of mathematical sociology.
[23] Matthew L. Jockers. Macroanalysis: Digital Methods and Literary History , 2013 .
[24] Arkaitz Zubiaga,et al. Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads , 2015, PloS one.
[25] M. Kirschenbaum. The Remaking of Reading : Data Mining and the Digital Humanities , 2007 .
[26] Dong Nguyen,et al. Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment , 2014, COLING.
[27] A. N. Beard. We Don't Know What We Don't Know , 2003 .
[28] Dong Nguyen,et al. A Kernel Independence Test for Geographical Language Variation , 2016, CL.
[29] Emily M. Bender,et al. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science , 2018, TACL.
[30] Huan Liu,et al. Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.
[31] William L. Hamilton,et al. Language from police body camera footage shows racial disparities in officer respect , 2017, Proceedings of the National Academy of Sciences.
[32] M. Williams,et al. Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation , 2017, Sociology.
[33] Eric Gilbert,et al. The Internet's Hidden Rules , 2018, Proceedings of the ACM on Human-Computer Interaction.
[34] Emre Kıcıman,et al. Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries , 2018, Front. Big Data.
[35] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[36] Antske Fokkens,et al. Offspring from Reproduction Problems: What Replication Failure Teaches Us , 2013, ACL.
[37] J. Pennebaker,et al. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .
[38] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[39] Arkaitz Zubiaga,et al. Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations , 2016, COLING.
[40] Timothy R. Tangherlini. Big Folklore: A Special Issue on Computational Folkloristics , 2016 .
[41] D. Boyd,et al. CRITICAL QUESTIONS FOR BIG DATA , 2012 .
[42] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[43] D. Collier,et al. Measurement Validity: A Shared Standard for Qualitative and Quantitative Research , 2001, American Political Science Review.
[44] Brendan T. O'Connor,et al. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.
[45] Djoerd Hiemstra,et al. #SupportTheCause: Identifying Motivations to Participate in Online Health Campaigns , 2015, EMNLP.
[46] B. Hamm. Projections of Power: Framing News, Public Opinion, and U.S. Foreign Policy , 2004 .
[47] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[48] Scott Sanner,et al. Improving LDA topic models for microblogs via tweet pooling and automatic labeling , 2013, SIGIR.
[49] Dong Nguyen,et al. Automatic Enrichment and Classification of Folktales in the Dutch Folktale Database , 2016 .
[50] Dirk Hovy,et al. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter , 2016, NAACL.
[51] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[52] Christopher M. Danforth,et al. Characterizing the Google Books Corpus: Strong Limits to Inferences of Socio-Cultural and Linguistic Evolution , 2015, PloS one.
[53] Timothy Baldwin,et al. Lexical Normalisation of Short Text Messages: Makn Sens a #twitter , 2011, ACL.
[54] Shankar Kumar,et al. Normalization of non-standard words , 2001, Comput. Speech Lang..
[55] Justin Grimmer,et al. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.
[56] F. Mosteller,et al. Inference in an Authorship Problem , 1963 .
[57] P. Eckert. Age as a Sociolinguistic Variable , 2017 .
[58] Daniela Stockmann,et al. We Don't Know What We Don't Know: When and How the Use of Twitter's Public APIs Biases Scientific Inference , 2017 .
[59] Arkaitz Zubiaga,et al. Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter , 2016, ACL.
[60] Paul DiMaggio,et al. Adapting computational text analysis to social science (and vice versa) , 2015, Big Data Soc..
[61] Michael Piotrowski,et al. Natural Language Processing for Historical Texts , 2012, Synthesis Lectures on Human Language Technologies.
[62] Inioluwa Deborah Raji,et al. Model Cards for Model Reporting , 2018, FAT.
[63] Christopher Potts,et al. Enculturation Trajectories: Language, Cultural Adaptation, and Individual Outcomes in Organizations , 2015, Manag. Sci..
[64] Andrew Piper. Novel Devotions: Conversional Reading, Computational Modeling, and the Modern Novel , 2015 .
[65] Djoerd Hiemstra,et al. Predicting relevance based on assessor disagreement: analysis and practical applications for search evaluation , 2015, Information Retrieval Journal.
[66] J. Overhage,et al. Sorting Things Out: Classification and Its Consequences , 2001, Annals of Internal Medicine.
[67] Michael S. Bernstein,et al. Empath: Understanding Topic Signals in Large-Scale Text , 2016, CHI.
[68] Simon DeDeo,et al. Exploration and exploitation of Victorian science in Darwin’s reading notebooks , 2015, Cognition.
[69] Dirk Hovy,et al. Tagging Performance Correlates with Author Age , 2015, ACL.
[70] Hau L. Lee,et al. Socially and Environmentally Responsible Value Chain Innovations: New Operations Management Research Opportunities , 2017, Manag. Sci..
[71] Kimberly A. Neuendorf,et al. The Content Analysis Guidebook , 2001 .
[72] Timnit Gebru,et al. Datasheets for datasets , 2018, Commun. ACM.
[73] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[74] Arthur Spirling,et al. Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It , 2017, Political Analysis.
[75] Yejin Choi,et al. The Risk of Racial Bias in Hate Speech Detection , 2019, ACL.
[76] Brendan T. O'Connor,et al. Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.
[77] Günter Neumann,et al. Arabic Computational Morphology , 2007 .
[78] David M. Mimno,et al. Comparing Apples to Apple: The Effects of Stemmers on Topic Models , 2016, TACL.
[79] Taku Kudo,et al. SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing , 2018, EMNLP.
[80] Diyi Yang,et al. Seekers, Providers, Welcomers, and Storytellers: Modeling Social Roles in Online Health Communities , 2019, CHI.
[81] Scott A. Golder,et al. Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .
[82] Carolyn Penstein Rosé,et al. Computational Sociolinguistics: A Survey , 2016, Computational Linguistics.
[83] Günter Neumann,et al. Arabic Computational Morphology: Knowledge-based and Empirical Methods , 2007 .
[84] D. Campbell,et al. Unobtrusive Measures: Nonreactive Research in the Social Sciences , 1966 .
[85] Eric P. Xing,et al. Sparse Additive Generative Models of Text , 2011, ICML.
[86] Cristian Danescu-Niculescu-Mizil,et al. Conversations Gone Awry: Detecting Early Signs of Conversational Failure , 2018, ACL.
[87] Jacob Eisenstein,et al. You Can't Stay Here , 2017 .
[88] Douglas G Altman,et al. Dichotomizing continuous predictors in multiple regression: a bad idea , 2006, Statistics in medicine.
[89] Eric P. Xing,et al. Diffusion of Lexical Change in Social Media , 2012, PloS one.
[90] David M. Mimno,et al. Applications of Topic Models , 2017, Found. Trends Inf. Retr..
[91] Michael S. Bernstein,et al. Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions , 2017, CSCW.
[92] Hoyt Long,et al. Literary Pattern Recognition: Modernism between Close Reading and Machine Learning , 2016, Critical Inquiry.