Graduation Semester and Year
Summer 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Chengkai Li
Second Advisor
Gautam Das
Third Advisor
Won Hwa Kim
Fourth Advisor
Shirin Nilizadeh
Abstract
The ubiquity of social media has transformed it into a rich source for reflecting people's opinions, behaviors, and interactions. Users frequently encounter factual claims in news, stories, and political statements, which can be either true or false. These claims significantly shape people's minds and behaviors, influencing not only individual perspectives but also broader public discourse. This study explores individuals' behaviors and perceptions toward factual claims by leveraging the concept of "check-worthiness" to analyze the relationship between such claims and user behaviors across datasets containing tens of millions of social media posts, particularly tweets from the platform X (formerly Twitter). It addresses key questions, including whether users exhibit different posting tendencies based on the check-worthiness of claims, the underlying reasons for these differences, and whether users are more likely to engage with content that aligns with the check-worthiness levels of their own posts.
Furthermore, the research introduces Wildfire, an innovative social sensing platform that empowers laypersons to conduct social sensing tasks using Twitter data without requiring programming or data analytics skills. Unlike existing tools that rely on simple keyword-based searches, Wildfire employs a heuristic graph exploration method to selectively expand the collected tweet-account graph, enhancing the collection of task-relevant data. This platform also offers a range of analytic tools, such as text classification, topic generation, and entity recognition, facilitating tasks such as trend analysis and public opinion sensing.
In addition to these methodological advancements, the research includes several real-world case studies that contribute to understanding the surveillance and impact of factual claims on specific topics, particularly in the contexts of the COVID-19 pandemic and climate change discussions on social media. Utilizing large language models, the study matches tweets with curated facts and misinformation, analyzing their stances and spatio-temporal spread. The findings highlight trends in misinformation during the COVID-19 pandemic and reveal the public's general tendency to believe claims regardless of their veracity.
In summary, the integration of social media into our daily lives has profoundly transformed how we interact with information, necessitating sophisticated approaches to understanding and managing the vast array of factual claims circulating online. The rise of platforms like Twitter has amplified the impact of these factual claims on public opinion and major societal events. By adopting the innovative approach of claim sensing, this study aims to bridge the gap between factual claims and human behavior.
Keywords
Claim sensing, Factual claim, Social media analytics
Disciplines
Computer Sciences | Data Science
License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Recommended Citation
Zhang, Zeyu, "CLAIM SENSING: A STUDY LINKING FACTUAL CLAIMS TO HUMAN BEHAVIORS ON SOCIAL MEDIA" (2024). Computer Science and Engineering Dissertations. 260.
https://mavmatrix.uta.edu/cse_dissertations/260