Addressing Age-Related Bias in Sentiment Analysis

ABSTRACT

Computational approaches to text analysis are useful in understanding aspects of online interaction, such as opinions and subjectivity in text. Yet, recent studies have identified various forms of bias in language-based models, raising concerns about the risk of propagating social biases against certain groups based on sociodemographic factors (e.g., gender, race, geography). In this study, we contribute a systematic examination of the application of language models to study discourse on aging. We analyze the treatment of age-related terms across 15 sentiment analysis models and 10 widely-used GloVe word embeddings and attempt to alleviate bias through a method of processing model training data. Our results demonstrate that significant age bias is encoded in the outputs of many sentiment analysis algorithms and word embeddings. We discuss the models' characteristics in relation to output bias and how these models might be best incorporated into research.

References

1. Paul Baker and Amanda Potts. 2013. "Why do white people have thin lips?" Google and the perpetuation of stereotypes via auto-complete search forms. Critical Discourse Studies 10, May 2015: 187--204.
2. Shaowen Bardzell. 2010. Feminist HCI: Taking Stock and Outlining an Agenda for Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10), 1301--1310.
3. Shaowen Bardzell and Jeffrey Bardzell. 2011. Towards a Feminist HCI Methodology: Social Science, Feminism, and HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11), 675--684.
4. Eric P.S. Baumer, Xiaotong Xu, Christine Chu, Shion Guha, and Geri K. Gay. 2017. When Subjects Interpret the Data: Social Media Non-use as a Case for Adapting the Delphi Method to CSCW. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17): 1527--1543.
5. Michael S Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer, and Menlo Park. 2013. Quantifying the Invisible Audience in Social Networks. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13): 21--30.
6. Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016. Quantifying and Reducing Stereotypes in Word Embeddings. arXiv preprint.
7. Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In 30th Conference on Neural Information Processing Systems (NIPS 2016).
8. Danah Boyd, Karen Levy, and Alice Marwick. 2014. The Networked Nature of Algorithmic Discrimination. Data and Discrimination: Collected Essays. Open Technology Institute.
9. Robin Brewer, Meredith Ringel Morris, and Anne Marie Piper. 2016. "Why would anybody do this?": Understanding Older Adults' Motivations and Challenges in Crowd Work. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16): 2246--2257.
10. Robin Brewer and Anne Marie Piper. 2016. "Tell It Like It Really Is": A Case of Online Content Creation and Sharing Among Older Adult Bloggers. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16), 5529--5542.
11. Alexander Budanitsky and Graeme Hirst. 2006. Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32, 1.
12. Robert N. Butler. 1969. Age-ism: Another form of bigotry. Gerontologist 9, 4: 243--246.
13. Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora necessarily contain human biases. Science 356: 183--186.
14. Aylin Caliskan-islam, Joanna J Bryson, and Arvind Narayanan. 2016. Semantics derived automatically from language corpora necessarily contain human biases. arXiv:1608.07187v2 {cs.AI} 30 Aug 2016: 1--14.
15. Le Chen, Alan Mislove, and Christo Wilson. 2015. Peeking Beneath the Hood of Uber. Proceedings of the 2015 Internet Measurement Conference (IMC '15): 495--508.
16. Kate Crawford. 2016. Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics. Science, Technology, & Human Values 41, 1: 77--92.
17. Kimberle Crenshaw. 1991. Mapping the Margins: Intersectionality, Identity Politics, and Violence Against Women of Color. Stanford Law Review 43, 6: 1241--1299.
18. Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010. Enhanced sentiment learning using Twitter hashtags and smileys. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (COLING '10), 241--249.
19. Mark Davies. 2008. The Corpus of Contemporary American English (COCA): 520 million words, 1990present. BYE, Brigham Young University.
20. Michael A Devito. 2016. From Editors to Algorithms. Digital Journalism: 1--21.
21. Michael Devito, Darren Gergle, and Jeremy Birnholtz. 2017. "Algorithms ruin everything": # RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), In press.
22. N Diakopoulos. 2014. Algorithmic accountability reporting: On the investigation of black boxes. Tow Center for Digital Journalism: A Tow/Knight Brief.
23. Lynn Dombrowski, Ellie Harmon, and Sarah Fox. 2016. Social Justice-Oriented Interaction Design: Outlining Key Design Strategies and Commitments. Proceedings of the Designing Interactive Systems Conference (DIS '16): 656--671.
24. Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik. 2016. First I "like" it, then I hide it: Folk Theories of Social Feeds. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16), 2371--2382.
25. Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. "I always assumed that I wasn't really that close to {her}": Reasoning about Invisible Algorithms in News Feeds. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15): 153--162.
26. Ronen Feldman. 2013. Techniques and applications for sentiment analysis. Communications of the ACM 56, 4: 82--89.
27. Batya Friedman and Helen Nissenbaum. 1996. Bias in computer systems. ACM Transactions on Information Systems 14, 3: 330--347.
28. Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1, 12.
29. Anthony G Greenwald, Debbie E Mcghee, and Jordan L K Schwartz. 1998. Measuring Individual Differences in Implicit Cognition: The Implicit Association Test. Journal of Personality and Soclal Psychology 74, 6: 1464--1480.
30. Philip J Guo. 2017. Older Adults Learning Computer Programming: Motivations, Frustrations, and Design Opportunities. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '17).
31. Dave Harley and Geraldine Fitzpatrick. 2009. YouTube and intergenerational communication: the case of Geriatric1927. Universal Access in the Information Society 8, 1: 5--20.
32. Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04), 168-- 177.
33. Eric H. Huang, Richard Socher, Christopher D. Manning, and Andrew Y. Ng. 2012. Improving word representations via global context and multiple word prototypes. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1 (ACL '12) Vol. 1.
34. Mary Lee Hummert, Teri A. Garstka, Laurie T. O'Brien, Anthony G. Greenwald, and Deborah S. Mellott. 2002. Using the Implicit Association Test to measure age differences in implicit social cognitions. Psychology and Aging 17, 3: 482--495.
35. CJ J. Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International AAAI Conference on Weblogs and Social Media, 216--225.
36. Lucas D. Introna and Helen Nissenbaum. 2000. Shaping the Web: why the politics of search engines matters. The Information Society 16: 169--185.
37. Lucas D Introna and David Wood. 2004. Picturing Algorithmic Surveillance: The Politics of Facial Recognition Systems. Surveillance & Society: CCTV Special Issue 2, 2/3.
38. Lucas Introna and Helen Nissenbaum. 2000. Defining the Web: The Politics of Search Engines. Computer 33, 54--62.
39. Lilly Irani, Janet Vertesi, and Paul Dourish. 2010. Postcolonial computing: a lens on design and development. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10): 1311--1320.
40. Isaac Johnson, Connor McMahon, Johannes Schöning, and Brent Hecht. 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 35th Annual ACM Conference on Human Factors in Computing Systems (CHI '17).
41. Matthew Kay, Cynthia Matuszek, and Sean a. Munson. 2015. Unequal Representation and Gender Stereotypes in Image Search Results for Occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15), 3819--3828.
42. Rob Kitchin. 2017. Thinking critically about and researching algorithms. Information, Communication & Society 20, 1.
43. Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, and Karrie Krahalios. 2017. Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17), 417--432.
44. Nathan R. Kuncel, Deniz S. Ones, and David M. Klieger. 2014. In Hiring, Algorithms Beat Instinct. Harvard Business Review May.
45. Joanna N. Lahey. 2010. International Comparison of Age Discrimination Laws. Research on Aging 32, 6: 679--697.
46. K.P. Lasher and P.J. Faulkender. 1993. Measurement of Aging Anxiety: Development of the Anxiety About Aging Scale. The International Journal of Aging and Human Development 37, 4: 247--259.
47. Amanda Lazar, Mark Diaz, Robin Brewer, Chelsea Kim, and Anne Marie Piper. 2017. Going Gray, Failure to Hire, and the Ick Factor: Analyzing How Older Bloggers Talk about Ageism. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW '17).
48. Amanda Lazar, Caroline Edasis, and Anne Marie Piper. 2017. A Critical Lens on Dementia and Design in HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), In press.
49. Becca Levy. 2009. Stereotype Embodiment: A Psychosocial Approach to Aging. Current directions in psychological science 18, 6: 332--336.
50. Q. Vera Liao, Wai-Tat Fu, and Markus Strohmaier. 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), 3352--3363.
51. Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1 (HLT '11).
52. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS'13), 3111-- 3119.
53. Boaz Miller and Isaac Record. 2016. Responsible epistemic technologies: A social-epistemological analysis of autocompleted web search. new media & society: 1--19.
54. Claire Cain Miller. 2015. Can an Algorithm Hire Better Than a Human. The New York Times.
55. Karine Nahon. 2015. Where there is Social Media there is Politics. In Forthcoming in Routledge Companion to Social Media and Politics, A. Bruns, E. Skogerbo, C. Christensen, O.A. Larsson and G.S. Enli (eds.). Routledge, NYC, NY.
56. Helen Nissenbaum. How Computer Systems Embody Values. Computer 34, 3: 118--119.
57. Alana Officer, Mira Leonie Schneiders, Diane Wu, Paul Nash, Jotheeswaran Amuthavalli Thiyagarajan, and John R. Beard. 2016. Valuing older people: Time for a global campaign to combat ageism. Bulletin of the World Health Organization 94, 709--784.
58. Bo Pang and Lillian Lee. 2006. Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval 1, 2: 91--231.
59. Frank Pasquale. 2015. The Black Box Society: The Secret Algorithms That Control Money. Harvard University Press.
60. Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: 1532--1543.
61. Pew Research Center. 2014. Older Adults and Technology Use. April. https://doi.org/202.419.4500
62. Filipe N. Ribeiro, Matheus Ara??jo, Pollyanna Gon??alves, Marcos Andr?? Gon??alves, and Fabr??cio Benevenuto. 2016. SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science 5, 1: 1--29.
63. Jennifer A. Rode. 2011. A theoretical agenda for feminist HCI. Interacting with Computers 23, 5: 393-- 400.
64. Shilad Sen, Margaret E Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao "Ken" Wang, and Brent Hecht. 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), 826--838.
65. Thomas Smyth and Jill Dimond. 2014. Anti-Oppressive Design. interactions 21, 6: 68--71.
66. Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the conference on empirical methods in natural language processing (EMNLP), 1631--1642.
67. Kate Starbird and Leysia Palen. 2012. (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising. Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12): 7--16.
68. L Sweeney. 2013. Discrimination in online ad delivery. acmqueue 11, 3.
69. Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. LexiconBased Methods for Sentiment Analysis. Computational Linguistics 37, 2: 267--307.
70. Antonio Torralba and Alexai A. Efros. 2011. Unbiased look at dataset bias. In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
71. Zeynep Tufekci. 2014. Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency. Journal on Telecommunications and High Technology Law 13: 203--218.
72. Marlon Twyman, Brian C. Keegan, and Aaron Shaw. 2016. Black Lives Matter in Wikipedia: Collaboration and Collective Memory around Online Social Movements. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '17), 1400--1412.
73. W. N. Venables and B. D. Ripley. 2002. Modern Applied Statistics with S. Springer, New York.
74. John Vines, Gary Pritchard, Peter Wright, Patrick Olivier, and Katie Brittain. 2015. An Age-Old Problem: Examining the Discourses of Ageing in HCI and Strategies for Future Research. ACM Transactions on Computer-Human Interaction 22, 1.
75. Claudia Wagner, Eduardo Graells-Garrido, David Garcia, and Filippo Menczer. 2016. Women through the glass ceiling: gender asymmetries in Wikipedia. EPJ Data Science 5, 1.
76. Theresa Wilson, Paul Hoffmann, Swapna Somasundaran, Jason Kessler, Janyce Wiebe, Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan. OpinionFinder: A system for subjectivity analysis. In Proceedings of hlt/emnlp on interactive demonstrations, 34--35.

Index Terms

• Human-centered computing