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PreprintCited by 763Ethics and Social Impacts of AIBig Data Technologies and ApplicationsComputational and Text Analysis Methods

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

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Olteanu, Alexandra, Castillo, Carlos, Diaz, Fernando, Kiciman, Emre·2016·Frontiers in Big Data
DOI 10.2139/ssrn.2886526PMC PMC7931947
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Abstract

Social data in digital form, which includes user-generated content, expressed or implicit relationships between people, and behavioral traces, are at the core of many popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, product, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This survey recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We present a framework for identifying a broad variety of menaces in the research and practices around social data use.

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  1. Acquisti, A., and Gross, R. (2006). "Imagined communities: awareness, information sharing, and privacy on the facebook, " in Proceedings of the 6th International Conference on Privacy Enhancing Technologies, PET'06 (Berlin; Heidelberg: Springer-Verlag), 36-58.
  2. Almuhimedi, H., Wilson, S., Liu, B., Sadeh, N., and Acquisti, A. (2013). "Tweets are forever: a large-scale quantitative analysis of deleted tweets, " in Proceedings of the 2013 Conference on Computer Supported Cooperative Work, CSCW '13 (New York, NY: ACM), 897-908.
  3. Baeza-Yates, R. A. (2013). "Big data or right data?, " in Proceedings of the 7th Alberto Mendelzon International Workshop on Foundations of Data Management (Puebla; Cholula).
  4. Barocas, S. (2014). "Data mining and the discourse on discrimination, " in KDD Workshop on Data Ethics (New York, NY: ACM).
  5. Benton, A., Coppersmith, G., and Dredze, M. (2017). "Ethical research protocols for social media health research, " in Proceeding of ACL Workshop on Ethics in NLP (Valencia).
  6. Bhattacharya, P., Ghosh, S., Kulshrestha, J., Mondal, M., Zafar, M. B., Ganguly, N., et al. (2014). "Deep twitter diving: Exploring topical groups in microblogs at scale, " in Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW '14 (New York, NY: ACM), 197-210.
  7. Blodgett, S. L., Green, L., and O'Connor, B. (2016). "Demographic dialectal variation in social media: a case study of African-American English, " in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Austin, TX: Association for Computational Linguistics), 1119- 1130.
  8. boyd, D., and Crawford, K. (2012). Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inform. Commun. Soc. 15, 662-679. doi: 10.1080/1369118X.2012.678878 · DOI · Paper page
  9. Buolamwini, J., and Gebru, T. (2018). "Gender shades: intersectional accuracy disparities in commercial gender classification, " in Proceeding of FAT* (New York, NY).
  10. Burke, M., Adamic, L., and Marciniak, K. (2013). "Families on facebook, " in International AAAI Conference on Web and Social Media (Boston, MA).
  11. Burke, M., and Kraut, R. E. (2014). "Growing closer on facebook: Changes in tie strength through social network site use, " in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '14 (New York, NY: ACM), 4187-4196.
  12. Chakraborty, A., Messias, J., Benevenuto, F., Ghosh, S., Ganguly, N., and Gummadi, K. P. (2017). "Who makes trends? understanding demographic biases in crowdsourced recommendations, " in Proceedings of International AAAI Conference on Web and Social Media (Montreal, QC).
  13. Chang, D., Krupka, E. L., Adar, E., and Acquisti, A. (2016). "Engineering information disclosure: norm shaping designs, " in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI '16 (New York, NY: ACM), 587-597.
  14. Chen, J., Nairn, R., Nelson, L., Bernstein, M., and Chi, E. (2010). "Short and tweet: experiments on recommending content from information streams, " in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10 (New York, NY: ACM), 1185-1194.
  15. Chu, Z., Gianvecchio, S., Wang, H., and Jajodia, S. (2012). Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Depend. Secure Comput. 9, 811-824. doi: 10.1109/TDSC.2012. · DOI · Paper page
  16. Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., and Potts, C. (2013). "No country for old members: user lifecycle and linguistic change in online communities, " in Proceedings of the 22Nd International Conference on World Wide Web, WWW '13 (New York, NY: ACM), 307-318.
  17. Datta, A., Tschantz, M. C., and Datta, A. (2015). Automated experiments on ad privacy settings. Proc. Privacy Enhanc. Technol. 2015, 92-112.
  18. De Choudhury, M., Morris, M. R., and White, R. W. (2014). "Seeking and sharing health information online: Comparing search engines and social media, " in Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems, CHI '14 (New York, NY: ACM), 1365-1376.
  19. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). "Fairness through awareness, " in Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS '12 (New York, NY: ACM), 214-226.
  20. Fraustino, J. D., Liu, B., and Jin, Y. (2012). Social Media Use During Disasters: A Review of the Knowledge Base and Gaps. Technical Report, Science and Technology Directorate; U.S. Department of Homeland Security.
  21. Friedman, B., and Nissenbaum, H. (1996). Bias in computer systems. ACM Trans. Inform. Syst. 14, 330-347.
  22. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Hal Daumé, I., et al. (2018). Datasheets for datasets. arXiv:1803.09010. · arXiv
  23. Green, B. (2018). Data science as political action: Grounding data science in a politics of justice. arXiv preprint arXiv:1811.03435. · arXiv
  24. Gyongyi, Z., and Garcia-Molina, H. (2005). "Web spam taxonomy, " in First International Workshop on Adversarial Information Retrieval on the Web (AIRWeb 2005) (Tokyo).
  25. Hecht, B., and Stephens, M. (2014). "A tale of cities: urban biases in volunteered geographic information, " in International AAAI Conference on Web and Social Media (Ann Arbor, MI).
  26. Heidari, H., Ferrari, C., Gummadi, K., and Krause, A. (2018). "Fairness behind a veil of ignorance: a welfare analysis for automated decision making, " in Advances in Neural Information Processing Systems 31, eds S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Montreal, QC: Curran Associates, Inc), 1265-1276.
  27. Hind, M., Mehta, S., Mojsilovic, A., Nair, R., Ramamurthy, K. N., Olteanu, A., et al. (2018). Increasing trust in ai services through supplier's declarations of conformity. arXiv preprint arXiv:1808.07261. · arXiv
  28. Holstein, K., Wortman Vaughan, J., Daumé, H. III., Dudik, M., and Wallach, H. (2019). "Improving fairness in machine learning systems: what do industry practitioners need?, " in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI '19 (New York, NY: ACM), 600:1-600:16.
  29. Hovy, D., and Spruit, S. L. (2016). "The social impact of natural language processing, " in Proceeding of ACL (Berlin).
  30. Java, A., Song, X., Finin, T., and Tseng, B. (2007). "Why we twitter: Understanding microblogging usage and communities, " in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, WebKDD/SNA-KDD '07 (New York, NY: ACM), 56-65.
  31. Johnson, I., McMahon, C., Schöning, J., and Hecht, B. (2017). "The effect of population and "structural" biases on social media-based algorithms: a case study in geolocation inference across the urban-rural spectrum, " in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI '17 (New York, NY: ACM), 1167-1178.
  32. Joinson, A. N. (2008). "Looking at, looking up or keeping up with people?: Motives and use of facebook, " in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '08 (New York, NY: ACM), 1027-1036.
  33. Joseph, K., Friedland, L., Hobbs, W., Lazer, D., and Tsur, O. (2017). "Constance: modeling annotation contexts to improve stance classification, " in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Copenhagen), 1115-1124.
  34. Kearns, M., Roth, A., and Wu, Z. S. (2017). "Meritocratic fairness for cross- population selection, " in Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, eds D. Precup,and Y. W. Teh (Sydney, NSW: PMLR; International Convention Centre), 1828-1836.
  35. Kıcıman, E., and Richardson, M. (2015). "Towards decision support and goal achievement: Identifying action-outcome relationships from social media, " in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15 (New York, NY: ACM), 547-556.
  36. Kleinberg, J. M., Mullainathan, S., and Raghavan, M. (2017). "Inherent trade-offs in the fair determination of risk scores, " in Proceeding of Innovations in Theoretical Computer Science (Berkeley, CA).
  37. Lee, M. K., and Baykal, S. (2017). "Algorithmic mediation in group decisions: Fairness perceptions of algorithmically mediated vs. discussion-based social division, " in Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW '17 (New York, NY: ACM), 1035-1048.
  38. Li, C., Wang, Y., Resnick, P., and Mei, Q. (2014). "Req-rec: high recall retrieval with query pooling and interactive classification, " in Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR '14 (New York, NY: ACM), 163-172.
  39. Liao, Q. V., Fu, W.-T., and Strohmaier, M. (2016). "#snowden: Understanding biases introduced by behavioral differences of opinion groups on social media, " in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI '16 (New York, NY: ACM), 3352-3363.
  40. Linder, F. (2017). Improved Data Collection From Online Sources Using Query Expansion and Active Learning. Available at SSRN: https://ssrn.com/abstract= 3026393
  41. Maddock, J., Starbird, K., and Mason, R. (2015). "Using historical Twitter data for research: Ethical challenges of tweet deletions, " in Proceeding of CSCW Workshop on Ethics (Vancouver, BC).
  42. Matias, J., Johnson, A., Boesel, W. E., Keegan, B., Friedman, J., and DeTar, C. (2015). Reporting, Reviewing, and Responding to Harassment on Twitter. Available online at SSRN 2602018.
  43. Mehrotra, R., Anderson, A., Diaz, F., Sharma, A., Wallach, H., and Yilmaz, E. (2017). "Auditing search engines for differential satisfaction across demographics, " in Proceedings of the 26th International Conference on World Wide Web Companion, WWW '17 Companion (Perth, WA: International World Wide Web Conferences Steering Committee), 626-633.
  44. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T. (2019). "Model cards for model reporting, " in Proceeding of FAT* (Atlanta, GA).
  45. Morstatter, F., Dani, H., Sampson, J., and Liu, H. (2016). "Can one tamper with the sample api?: toward neutralizing bias from spam and bot content, " in Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16 Companion (Montreal, QC: International World Wide Web Conferences Steering Committee), 81-82.
  46. Narayanan, A. (2018). "Translation tutorial: 21 fairness definitions and their politics, " in Proceeding of FAT* (New York, NY).
  47. Narayanan, A., and Shmatikov, V. (2008). "Robust de-anonymization of large sparse datasets, " in Proceeding of IEEE Symposium on Security and Privacy (Oakland, CA).
  48. Narayanan, A., and Zevenbergen, B. (2015). "No Encore for Encore? ethical questions for web-based censorship measurement, " in Ethical Questions for Web-Based Censorship Measurement. Available online at: http://ssrn.com/ abstract=2665148
  49. Naveed, N., Gottron, T., Kunegis, J., and Alhadi, A. C. (2011). "Searching microblogs: coping with sparsity and document quality, " in Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM '11 (New York, NY: ACM), 183-188.
  50. Olteanu, A., Talamadupula, K., and Varshney, K. R. (2017a). "The limits of abstract evaluation metrics: The case of hate speech detection, " in Proceedings of the 2017 ACM on Web Science Conference, WebSci '17 (New York, NY: ACM), 405-406.
  51. Olteanu, A., Varol, O., and Kıcıman, E. (2017b). "Distilling the outcomes of personal experiences: a propensity-scored analysis of social media, " in Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW '17 (New York, NY: ACM), 370-386.
  52. Olteanu, A., Vieweg, S., and Castillo, C. (2015). "What to expect when the unexpected happens: Social media communications across crises, " in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (Vancouver, BC: ACM), 994-1009.
  53. Patton, D., Blandfort, P., Frey, W., Gaskell, M., and Karaman, S. (2019). "Annotating social media data from vulnerable populations: evaluating disagreement between domain experts and graduate student annotators, " in Proceedings of the 52nd Hawaii International Conference on System Sciences, 2142-2151.
  54. Peddinti, S. T., Ross, K. W., and Cappos, J. (2014). "on the internet, nobody knows you're a dog": a twitter case study of anonymity in social networks, " in Proceedings of the Second ACM Conference on Online Social Networks, COSN '14 (New York, NY: ACM), 83-94.
  55. Poblete, B., Garcia, R., Mendoza, M., and Jaimes, A. (2011). "Do all birds tweet the same?: Characterizing twitter around the world, " in Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM '11 (New York, NY: ACM), 1025-1030.
  56. Poirier, L. (2018). Knowledge Representation in Scruffy Worlds an Ethnography of Semiotic Infrastructure Design Work (Ph.D thesis). Rensselaer Polytechnic Institute.
  57. Potts, L., Seitzinger, J., Jones, D., and Harrison, A. (2011). "Tweeting disaster: hashtag constructions and collisions, " in Proceedings of the 29th ACM International Conference on Design of Communication, SIGDOC '11 (New York, NY: ACM), 235-240.
  58. Preist, C., Massung, E., and Coyle, D. (2014). "Competing or aiming to be average?: normification as a means of engaging digital volunteers, " in Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW '14 (New York, NY: ACM), 1222-1233.
  59. Pury, C. L. (2011). Automation can lead to confounds in text analysis back, küfner, and egloff (2010) and the not-so-angry americans. Psychol. Sci. 22, 836-836. doi: 10.1177/0956797611408735 · DOI
  60. Quattrone, G., Capra, L., and De Meo, P. (2015). "There's no such thing as the perfect map: quantifying bias in spatial crowd-sourcing datasets, " in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW '15 (New York, NY: ACM), 1021-1032.
  61. Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A., et al. (2011). "Truthy: mapping the spread of astroturf in microblog streams, " in Proceedings of the 20th International Conference Companion on World Wide Web, WWW '11 (New York, NY: ACM), 249-252.
  62. Resnick, P., Garrett, R. K., Kriplean, T., Munson, S. A., and Stroud, N. J. (2013). "Bursting your (filter) bubble: strategies for promoting diverse exposure, " in Proceedings of the 2013 Conference on Computer Supported Cooperative Work Companion, CSCW '13 (New York, NY: ACM), 95-100.
  63. Romero, D. M., Meeder, B., and Kleinberg, J. (2011). "Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter, " in Proceedings of the 20th International Conference on World Wide Web, WWW '11 (New York, NY: ACM), 695-704.
  64. Rost, M., Barkhuus, L., Cramer, H., and Brown, B. (2013). "Representation and communication: challenges in interpreting large social media datasets, " in Proceedings of the 2013 Conference on Computer Supported Cooperative Work, CSCW '13 (New York, NY: ACM), 357-362.
  65. Saez-Trumper, D., Castillo, C., and Lalmas, M. (2013). "Social media news communities: gatekeeping, coverage, and statement bias, " in Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, CIKM '13 (New York, NY: ACM), 1679-1684.
  66. Sampson, J., Morstatter, F., Maciejewski, R., and Liu, H. (2015). "Surpassing the limit: keyword clustering to improve twitter sample coverage, " in Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT '15 (New York, NY: ACM), 237-245.
  67. Schoenebeck, G. (2013a). "Potential networks, contagious communities, and understanding social network structure, " in Proceedings of the 22nd International Conference on World Wide Web, WWW '13 (New York, NY: ACM), 1123-1132.
  68. Sen, S., Giesel, M. E., Gold, R., Hillmann, B., Lesicko, M., Naden, S., et al. (2015). "Turkers, scholars, "arafat" and "peace": cultural communities and algorithmic gold standards, " in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW '15 (New York, NY: ACM), 826-838.
  69. Skeels, M. M., and Grudin, J. (2009). "When social networks cross boundaries: a case study of workplace use of facebook and linkedin, " in Proceedings of the ACM 2009 International Conference on Supporting Group Work, GROUP '09 (New York, NY: ACM), 95-104.
  70. Starnini, M., Baronchelli, A., and Pastor-Satorras, R. (2016). Temporal correlations in social multiplex networks. arXiv preprint arXiv:1606.06626. · arXiv
  71. Tasse, D., Liu, Z., Sciuto, A., and Hong, J. (2017). "State of the geotags: Motivations and recent changes, " in International AAAI Conference on Web and Social Media (Montreal, QC).
  72. Taylor, S. J., Muchnik, L., and Aral, S. (2014). Identity and Opinion: A Randomized Experiment. Available at SSRN.
  73. Teevan, J., Ramage, D., and Morris, M. R. (2011). "#twittersearch: A comparison of microblog search and web search, " in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM '11 (New York, NY: ACM), 35-44.
  74. Thaler, R. H., and Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press.
  75. Thomas, K., Grier, C., Song, D., and Paxson, V. (2011). "Suspended accounts in retrospect: An analysis of twitter spam, " in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC '11 (New York, NY: ACM), 243-258.
  76. Trochim, W. M., and Donnelly, J. P. (2001). Research Methods Knowledge Base, Vol. 2. Cincinnati, OH: Atomic Dog Publishing.
  77. Van Dijck, J. (2013a). The Culture of Connectivity: A Critical History of Social Media. New York, NY: Oxford University Press.
  78. Varshney, K. R. (2015). Data science of the people, for the people, by the people: a viewpoint on an emerging dichotomy. in Proceeding of Bloomberg Data for Good Exchange (New York, NY).
  79. Verma, S., and Rubin, J. (2018). "Fairness definitions explained, " in Proceedings of the International Workshop on Software Fairness, FairWare '18 (New York, NY: ACM), 1-7.
  80. Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L. (2010). "Microblogging during two natural hazards events: what twitter may contribute to situational awareness, " in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10 (New York, NY: ACM), 1079-1088.
  81. Wagner, C., Mitter, S., Körner, C., and Strohmaier, M. (2012). "When social bots attack: Modeling susceptibility of users in online social networks, " in Proceedings of the WWW'12 Workshop on 'Making Sense of Microposts', Vol. 838, eds M. Rowe, M. Stankovic, and A. Dadzie (Lyon: CEUR-WS.org), 1-48.
  82. Wang, Y., Li, Y., and Luo, J. (2016b). "Deciphering the 2016 us presidential campaign in the twitter sphere: A comparison of the trumpists and clintonists, " in Proceedings of International AAAI Conference on Web and Social Media (Cologne).
  83. Wang, Y., Norcie, G., Komanduri, S., Acquisti, A., Leon, P. G., and Cranor, L. F. (2011). "'i regretted the minute i pressed share': A qualitative study of regrets on facebook, " in Proceedings of the Seventh Symposium on Usable Privacy and Security, SOUPS '11 (New York, NY: ACM), 10:1-10:16.
  84. White, R. W., Dumais, S. T., and Teevan, J. (2009). "Characterizing the influence of domain expertise on web search behavior, " in Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM '09 (New York, NY: ACM), 132-141.
  85. Zafar, M. B., Bhattacharya, P., Ganguly, N., Gummadi, K. P., and Ghosh, S. (2015). Sampling content from online social networks: comparing random vs. expert sampling of the twitter stream. ACM Trans. Web 9, 12:1-12:33. doi: 10.1145/2743023 · DOI

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Authors
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E.
Affiliations
  • Microsoft Research New York City (United States) (US)
  • Universitat Pompeu Fabra (ES)
Corresponding author
Olteanu, Alexandra
ORCID
Venue
Frontiers in Big Data
Publisher
Elsevier BV
Published
2016
Volume
2
Pages
13-13
ISSN
2624-909X (ISSN-L)
DOI
10.2139/ssrn.2886526
PMC
PMC7931947
OpenAlex
W2563852449
Cited by
763
References
308
Citations by year
2026: 86 · 2025: 86 · 2024: 117 · 2023: 126 · 2022: 105 · 2021: 122 · 2020: 73 · 2019: 25 · 2018: 17 · 2017: 4 · 2012: 2
FWCI
21.35
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Big Data (publishedVersion, cc-by)
  • SSRN Electronic Journal (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
social data, data bias, methodological pitfalls, data ethics, user-generated content, behavioral traces, data quality, research rigor, unintended consequences

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