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We propose a novel website structure based domain-level fake news detection model that has performance results surprisingly comparable to that of existing content-based methods. Through feature analysis, we highlight that fake news sites have more clustered subpages and more ads links, whereas traditional news sites are more substantive and more likely to contain staff links. We also demonstrate that the performance of existing content-based models improve significantly by incorporating structural features, particularly when the definitions for fake and traditional news sites are lax. This project is currently between submissions. Download
Collaborators: Ceren Budak
We build nested supervised learning models and identify more than 50 thousand social movement organizations (SMO) participating in 2 distinct movements on Twitter. We analyze SMOs' role from five different perspectives: commitment, knowledge sharing, community-building, network structural significance, and recruitment. This project is currently between submissions. Download
Collaborators: Ceren Budak
We measure the value of slacktivists involved in more than 80 protest movement hashtags on Twitter, focusing on their value for content generation, communication, encouragement, and content diversity. Further, we move beyond the existing literature which primarily focuses on direct content contributions by investigating whether slacktivists induce further contributions from other activists by providing them simple cues of encouragement that enhance non-slacktivists' commitment to a movement. This project was published in ICWSM2017. Download
Collaborators: Ceren Budak