My initial research idea was related to the moderation of social media misinformation and which methods would create or maintain the trustworthiness of those platforms. I had a number of questions related to this topic and have primarily decided to focus on user perception of different moderation methods. The application of specific methods and their effectiveness has received some attention but seemed to be an overly broad topic.
The primary goal of my proposed research is to assess social media users’ perceptions of social media moderation methods they view as most fair, effective, and trustworthy. Additionally, their perceptions of the need to moderate misinformation could be analyzed.
I’ve found a fair amount of research related to social media moderation as well as misinformation. Brandtzaeg and Følstad (2017) addressed trust in online fact-checking services and found that users often expressed concerns about the bias of the services themselves. Their discussion of the results included a recommendation for transparency in their processes and sources which is a concept that may also apply to other moderation methods.
Several databases available through the Forsyth Library are helpful in my research:
- Applied Science & Technology Full-Text
- Communication & Mass Media Complete
- Computer Source
These databases are all related to technology and had many results that were relevant to my research topic. The Communication and Mass Media database specifically had some interesting results related to misinformation.
Research References:
Allison, P. R. (2020). Black Lives Matter, But Do Bots Know That? Automated moderation to block offensive and objectionable online material only works if systems are configured and reviewed adequately. Computer Weekly, 23–27.
Badke, W. (2020). Evidence and the Search for Truth. Online Searcher, 44(4), 36–38.
Brandtzaeg, P. B., & Følstad, A. (2017). Trust and Distrust in Online Fact-Checking Services. Communications of the ACM, 60(9), 65–71.
Cerf, V. G. (2018). Unintended Consequences. Communications of the ACM, 61(3), 7.
Chen, J., Xu, H., & Whinston, A. B. (2011). Moderated Online Communities and Quality of User-Generated Content. Journal of Management Information Systems, 28(2), 237–268.
Gimpel, H., Heger, S., Olenberger, C., & Utz, L. (2021). The Effectiveness of Social Norms in Fighting Fake News on Social Media. Journal of Management Information Systems, 38(1), 196–221.
John, J. N. (2021). In Social Media We Trust. MIT Technology Review, 124(4), 19.
Khan, M. L., & Idris, I. K. (2019). Recognise misinformation and verify before sharing: a reasoned action and information literacy perspective. Behaviour & Information Technology, 38(12), 1194–1212.
Kim, A., Moravec, P. L., & Dennis, A. R. (2019). Combating Fake News on Social Media with Source Ratings: The Effects of User and Expert Reputation Ratings. Journal of Management Information Systems, 36(3), 931–968.
Klonick, K. (2021). Content Moderation Modulation: Deliberating on how to regulate–or not regulate–online speech in the era of evolving social media. Communications of the ACM, 64(1), 29–31.
Moravec, P. L., Minas, R. K., & Dennis, A. R. (2019). Fake News on Social Media: People Believe What They Want to Believe When It Makes No Sense at All. MIS Quarterly, 43(4), 1343–1360.
Savage, N. (2021). Fact-Finding Mission. Communications of the ACM, 64(3), 18–19.
Xinyi Zhou, & Zafarani, R. (2020). A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Computing Surveys, 53(5), 109–148.
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2018). Detection and Resolution of Rumours in Social Media: A Survey. ACM Computing Surveys, 51(2), 32:1-32:36.
I think it’ll be interesting to see what comes from your research. I’m curious on the methods used to determine trustworthiness of platforms and if you’ll find some bias in that itself. It seems like you have a lot of research to sift through though and I think that is fantastic and shows your on a great path for your project!