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Link to original content: https://doi.org/10.1007/978-3-030-85623-6_23
“Honestly I Never Really Thought About Adding a Description”: Why Highly Engaged Tweets Are Inaccessible | SpringerLink
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“Honestly I Never Really Thought About Adding a Description”: Why Highly Engaged Tweets Are Inaccessible

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Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

Abstract

Alternative (alt) text is vital for visually impaired users to consume digital images with screen readers. When these image descriptions are not incorporated, these users encounter accessibility challenges. In this study, we explore the prevalence and user understanding of alt text in Twitter. First, we assess the availability of alt text by collecting the Twitter Engagement (TWEN) dataset which contains over 1000 high engagement tweets regarding online articles from the most popular Google Keywords. We focused on keywords that create an engagement in Twitter in order to study the possibility of creating priorities of media content that missing alt text then adding descriptions to them by crowdsourcer to help the visually impaired to be equal like others in the social media communities. Our findings reveal approximately 91% of the tweets contained images and videos, less than 1% of the images had alt text. Thus, even highly engaged tweets remain inaccessible to visually impaired individuals. Thus, we designed two guided concepts to raise awareness of high engagement. We then surveyed 100 sighted participants to understand their perception of alt text and evaluate strategies to increase the frequency of alt text for highly engaged content. Our value-based guided concept was well received by the majority of the study participants.

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References

  1. Arora, A., Bansal, S., Kandpal, C., Aswani, R., Dwivedi, Y.: Measuring social media influencer index-insights from Facebook, Twitter and Instagram. J. Retail. Consum. Serv. 49, 86–101 (2019)

    Article  Google Scholar 

  2. Aslam, S.: Twitter by the numbers: Stats, demographics & fun facts (2020). https://www.omnicoreagency.com/twitter-statistics/

  3. Bennett, C.L., Keyes, O.: What is the point of fairness? Disability, AI and the complexity of justice. arXiv preprint arXiv:1908.01024 (2019)

  4. Bigham, J.P., et al.: VizWiz: nearly real-time answers to visual questions. In: Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology, pp. 333–342 (2010)

    Google Scholar 

  5. Bigham, J.P., Kaminsky, R.S., Ladner, R.E., Danielsson, O.M., Hempton, G.L.: WebInSight: making web images accessible. In: Proceedings of the 8th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 181–188 (2006)

    Google Scholar 

  6. Bigham, J.P., Ladner, R.E., Borodin, Y.: The design of human-powered access technology. In: The Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 3–10 (2011)

    Google Scholar 

  7. Brady, E., Morris, M.R., Bigham, J.P.: Gauging receptiveness to social microvol unteering. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1055–1064 (2015)

    Google Scholar 

  8. Brady, E.L., Zhong, Y., Morris, M.R., Bigham, J.P.: Investigating the appropriateness of social network question asking as a resource for blind users. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1225–1236 (2013)

    Google Scholar 

  9. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)

    Article  Google Scholar 

  10. Center, D.: Image sorting tool (2014). http://diagramcenter.org/decision-tree.html/

  11. Chiarella, D., Yarbrough, J., Jackson, C.A.L.: Using alt text to make science Twitter more accessible for people with visual impairments. Nat. Commun. 11(1), 1–3 (2020)

    Article  Google Scholar 

  12. Dorsey, J.: search+photos (2011). https://blog.twitter.com/en_us/a/2011/searchphotos.html

  13. Elzer, S., Schwartz, E., Carberry, S., Chester, D., Demir, S., Wu, P.: A browser extension for providing visually impaired users access to the content of bar charts on the web. In: WEBIST, no. 2, pp. 59–66 (2007)

    Google Scholar 

  14. Fang, H., et al.: From captions to visual concepts and back. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

    Google Scholar 

  15. Georges, V., Courtemanche, F., Senecal, S., Baccino, T., Fredette, M., Leger, P.M.: UX heatmaps: mapping user experience on visual interfaces. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 4850–4860 (2016)

    Google Scholar 

  16. Gleason, C., Carrington, P., Cassidy, C., Morris, M.R., Kitani, K.M., Bigham, J.P.: “It’s almost like they’re trying to hide it”: how user-provided image descriptions have failed to make Twitter accessible. In: The World Wide Web Conference, pp. 549–559 (2019)

    Google Scholar 

  17. Gleason, C., et al.: Twitter A11y: a browser extension to make Twitter images accessible. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2020)

    Google Scholar 

  18. Goddard, C.: Semantic Analysis: A Practical Introduction. Oxford University Press, Oxford (2011)

    Google Scholar 

  19. Gray, C.M., Kou, Y.: UX practitioners’ engagement with intermediate-level knowledge. In: Proceedings of the 2017 ACM Conference Companion Publication on Designing Interactive Systems, pp. 13–17 (2017)

    Google Scholar 

  20. Gray, C.M., Kou, Y., Battles, B., Hoggatt, J., Toombs, A.L.: The dark (patterns) side of UX design. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2018)

    Google Scholar 

  21. Grosser, B.: What do metrics want? How quantification prescribes social interaction on Facebook. Comput. Cult. 8(4), 1–8 (2014)

    Google Scholar 

  22. Guidelines, W.C.A.: How to meet WCAG (quick reference) (2019). https://www.w3.org/WAI/WCAG21/quickref/

  23. Guinness, D., Cutrell, E., Morris, M.R.: Caption crawler: enabling reusable alternative text descriptions using reverse image search. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–11 (2018)

    Google Scholar 

  24. Guo, A., Kamar, E., Vaughan, J.W., Wallach, H., Morris, M.R.: Toward fairness in AI for people with disabilities SBG@ a research roadmap. ACM SIGACCESS Accessibility Comput. 125, 1 (2020)

    Google Scholar 

  25. Julian Ausserhofer, A.M.: National politics on Twitter: structures and topics of a networked public sphere. Inf. Commun. Soc. 16(3), 291–314 (2013). https://doi.org/10.1080/1369118X.2012.756050

    Article  Google Scholar 

  26. Kemp, S.: Digital 2020: 3.8 billion people use social media (2020). https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media

  27. Kennedy, H., Hill, R.L.: The feeling of numbers: emotions in everyday engagements with data and their visualisation. Sociology 52(4), 830–848 (2018)

    Article  Google Scholar 

  28. Keysers, D., Renn, M., Breuel, T.M.: Improving accessibility of html documents by generating image-tags in a proxy. In: Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 249–250 (2007)

    Google Scholar 

  29. Kitson, A., Buie, E., Stepanova, E.R., Chirico, A., Riecke, B.E., Gaggioli, A.: Transformative experience design: designing with interactive technologies to support transformative experiences. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–5 (2019)

    Google Scholar 

  30. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  31. Loiacono, E.T., Romano, N.C., Jr., McCoy, S.: The state of corporate website accessibility. Commun. ACM 52(9), 128–132 (2009)

    Article  Google Scholar 

  32. MacLeod, H., Bennett, C.L., Morris, M.R., Cutrell, E.: Understanding blind people’s experiences with computer-generated captions of social media images. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 5988–5999 (2017)

    Google Scholar 

  33. Meeker, M.: Internet trends 2014 (2014). https://cryptome.org/2014/05/internet-trends-2014.pdf

  34. Whittaker, M., et al.: Disability, bias, and AI (2019). https://ainowinstitute.org/disabilitybiasai-2019.pdf

  35. Morris, M.R.: AI and accessibility: a discussion of ethical considerations. arXiv preprint arXiv:1908.08939 (2019)

  36. Morris, M.R., Zolyomi, A., Yao, C., Bahram, S., Bigham, J.P., Kane, S.K.: “ With most of it being pictures now, I rarely use it” understanding Twitter’s evolving accessibility to blind users. Presented at the (2016)

    Google Scholar 

  37. Obrist, M., Wurhofer, D., Beck, E., Karahasanovic, A., Tscheligi, M.: User experience (UX) patterns for audio-visual networked applications: inspirations for design. In: Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries, pp. 343–352 (2010)

    Google Scholar 

  38. Online, B.: Whatsapp users share 55 billion texts, 4.5 billion photos, 1 billion videos daily (2017). https://www.businesstoday.in/technology/news/whatsapp-users-share-texts-photos-videos-daily/story/257230.html

  39. Porter, T.M.: Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press, Princeton (1996)

    Book  Google Scholar 

  40. Power, C., Freire, A., Petrie, H., Swallow, D.: Guidelines are only half of the story: accessibility problems encountered by blind users on the web. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 433–442 (2012)

    Google Scholar 

  41. Ramnath, K., et al.: AutoCaption: automatic caption generation for personal photos. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1050–1057. IEEE (2014)

    Google Scholar 

  42. Rodríguez Vázquez, S.: Measuring the impact of automated evaluation tools on alternative text quality: a web translation study. In: Proceedings of the 13th Web for All Conference, pp. 1–10 (2016)

    Google Scholar 

  43. Rowe, N.C.: Marie-4: a high-recall, self-improving web crawler that finds images using captions. IEEE Intell. Syst. 17(4), 8–14 (2002)

    Article  Google Scholar 

  44. Salisbury, E., Kamar, E., Morris, M.R.: Toward scalable social alt text: conversational crowdsourcing as a tool for refining vision-to-language technology for the blind. In: 5th AAAI Conference on Human Computation and Crowdsourcing (2017)

    Google Scholar 

  45. Salisbury, E., Kamar, E., Morris, M.R.: Evaluating and complementing vision-to-language technology for people who are blind with conversational crowdsourcing. In: IJCAI, pp. 5349–5353 (2018)

    Google Scholar 

  46. Shaban, H.: Twitter reveals its daily active user numbers for the first time (2019). https://www.washingtonpost.com/technology/2019/02/07/twitter-reveals-its-daily-active-user-numbers-first-time/

  47. Shi, Y.: E-government web site accessibility in Australia and China: a longitudinal study. Soc. Sci. Comput. Rev. 24(3), 378–385 (2006)

    Article  Google Scholar 

  48. Siegemedia: The 100 most popular google keywords, March 2020. https://www.siegemedia.com/seo/most-popular-keywords

  49. Spyridonis, F., Daylamani-Zad, D.: A serious game to improve engagement with web accessibility guidelines. Behav. Inf. Technol. 39(4), 1–19 (2020). https://doi.org/10.1080/0144929X.2019.1711453

    Article  Google Scholar 

  50. Stangl, A., Morris, M.R., Gurari, D.: “ Person, shoes, tree. Is the person naked?” What people with vision impairments want in image descriptions. Presented at the (2020)

    Google Scholar 

  51. Strauss, A., Corbin, J.: Basics of Qualitative Research Techniques. Sage Publications, Thousand Oaks (1998)

    Google Scholar 

  52. todd: Accessible images for everyone (2016). https://blog.twitter.com/en_us/a/2016/accessible-images-for-everyone.html

  53. Tran, K., et al.: Rich image captioning in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016)

    Google Scholar 

  54. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)

    Google Scholar 

  55. Twitter: Rules and ltering: Premium (2020). https://developer.twitter.com/en/docs/twitter-api/premium/rules-and-ltering/operators-by-product

  56. Twitter: using-premium-operators (2020). https://developer.twitter.com/en/docs/twitter-api/premium/rules-and-filtering/using-premium-operators

  57. Twitter, I.: Promoted tweet (2020). https://business.twitter.com/en/help/campaign-setup/advertiser-card-specifications.html

  58. Twitter, I.: The suggested minimum properties for cards (2020). https://developer.twitter.com/en/docs/twitter-for-websites/cards/overview/summary-card-with-large-image

  59. @TwitterA11y: Adding descriptions to images (2020). https://twitter.com/TwitterA11y/status/1265689579371323392

  60. @TwitterBusiness: Fun fact: A study of twitter accounts (2020). https://twitter.com/TwitterBusiness/status/1070423034467540992?s=20

  61. Vilenchik, D.: The million tweets fallacy: activity and feedback are uncorrelated. In: Twelfth International AAAI Conference on Web and Social Media (2018)

    Google Scholar 

  62. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2015)

    Google Scholar 

  63. Von Ahn, L., Ginosar, S., Kedia, M., Liu, R., Blum, M.: Improving accessibility of the web with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 79–82 (2006)

    Google Scholar 

  64. Voykinska, V., Azenkot, S., Wu, S., Leshed, G.: How blind people interact with visual content on social networking services. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, pp. 1584–1595 (2016)

    Google Scholar 

  65. WHO: Blindness and vision impairment (2020). https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment

  66. Wood, G., et al.: Rethinking engagement with online news through social and visual co-annotation. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2018)

    Google Scholar 

  67. Wu, S., Adamic, L.A.: Visually impaired users on an online social network. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3133–3142 (2014)

    Google Scholar 

  68. Wu, S., Wieland, J., Farivar, O., Schiller, J.: Automatic alt-text: computer- generated image descriptions for blind users on a social network service. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 1180–1192 (2017)

    Google Scholar 

  69. Zhong, Y., Lasecki, W.S., Brady, E., Bigham, J.P.: RegionSpeak: quick comprehensive spatial descriptions of complex images for blind users. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2353–2362 (2015)

    Google Scholar 

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Acknowledgment

We thank Imam Abdulrahman Bin Faisal University (IAU) and Saudi Arabian Cultural Mission to the USA (SACM) for financially supporting Mallak Alkhathlan. We thank the US Department of Education P200A180088: GAANN grant and the WPI Data Science Department for financially supporting ML Tlachac. We thank Brittany Lewis and the WPI DAISY lab for their advice and support.

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Correspondence to Mallak Alkhathlan , M. L. Tlachac , Lane Harrison or Elke Rundensteiner .

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Alkhathlan, M., Tlachac, M.L., Harrison, L., Rundensteiner, E. (2021). “Honestly I Never Really Thought About Adding a Description”: Why Highly Engaged Tweets Are Inaccessible. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12932. Springer, Cham. https://doi.org/10.1007/978-3-030-85623-6_23

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