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Wildfire
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Wildfire Demo Video
We present Wildfire, an innovative social sensing platform designed for laypersons. The goal is to support users in conducting social sensing tasks using Twitter data without programming and data analytics skills. Existing open-source and commercial social sensing tools only support data collection using simple keyword-based or account-based search. On the contrary, Wildfire employs a heuristic graph exploration method to selectively expand the collected tweet-account graph in order to further retrieve more task-relevant tweets and accounts. This approach allows for the collection of data to support complex social sensing tasks that cannot be met with a simple keyword search. In addition, Wildfire provides a range of analytic tools, such as text classification, topic generation, and entity recognition, which can be crucial for tasks such as trend analysis. The platform also provides a web-based user interface for creating and monitoring tasks, exploring collected data, and performing analytics.
Wildfire system diagram
Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-Worthiness
This study examines how factual claims of different significance influence and reflect social media users' behavioral patterns. Leveraging "check-worthiness" as a measure of the factual significance of claims, we analyze the connection between factual claims and user behaviors on Twitter. Through a series of experiments using statistical methods such as correlation analysis and hypothesis testing, we provide insights into a few pivotal inquiries: (1) whether differences exist between users' tweeting tendencies toward check-worthiness, (2) the underlying reasons for such differences, (3) whether users tend to create, share, and endorse content with check-worthiness levels similar to their own tweets, and (4) whether users with similar tendencies toward check-worthiness exhibit heightened engagement. The experiments were conducted across three datasets, comprising over 48.5 million tweets and involving 15,000 users, spanning several domains and yielding statistically significant findings. Previous studies have primarily centered on examining the effectiveness and strategies of fact-checks rather than understanding people's behavioral tendencies toward factual claims. Our research pioneers understanding in this area, offering valuable insights for behavioral modeling and social sciences.