Social media is now an important source of everyday information. Given the plethora of scandals concerning the rapid spread of misinformation and disinformation on social media, the credibility of the content on these platforms is now a pivotal research area. Much of the existing work on social media credibility focuses on content credibility. In this study, however, we focus on the credibility of the profile as the virtual representation of the content author. We developed a real-time machine-learning-based online tool that assesses the credibility of profiles on Twitter, one of the most common and versatile social media platforms. To investigate user perceptions on credibility-related issues, we used our tool as a stimulus for people to reflect on their profile's credibility and collected 100 responses. The combination of our quantitative and qualitative analysis reveals that the latest tweets and retweet behavior are two of the most critical factors for profile credibility. It is also observed that people demonstrate a limited interest in their profile credibility but agree that the author's credibility is of paramount importance. With an open-source tool to assess user credibility on Twitter and a user study to establish its utility, we contribute a timely piece of research on the topic of online credibility.
|Title of host publication||CHIIR 2023 - Proceedings of the 2023 Conference on Human Information Interaction and Retrieval|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|State||Published - 19 Mar 2023|
|Event||8th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2023 - Austin, United States|
Duration: 19 Mar 2023 → 23 Mar 2023
|Name||CHIIR 2023 - Proceedings of the 2023 Conference on Human Information Interaction and Retrieval|
|Conference||8th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2023|
|Period||19/03/23 → 23/03/23|
Bibliographical noteFunding Information:
This research is supported by the Academy of Finland, Strategic Research Council, CRITICAL(335729). We gratefully thank Teemu Hannula, Joonas Ahti-Pekka Soudunsaari, and Roope Rajala, who contributed to the implementation of the tool.
This research is supported by the Academy of Finland, Strategic Research Council, CRITICAL(335729). We gratefully thank Teemu Hannula, Joonas Ahti- Pekka Soudunsaari, and Roope Rajala, who contributed to the implementation of the tool.
© 2023 Owner/Author.
- Machine Learning
- Profile Credibility
- Social Media