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Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or "fake news," present in these platforms. This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a...
This paper introduces a general technique, called LABurst, for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst...
This paper explores whether trust, developed in one context, transfers into another, distinct context and, if so, attempts to quantify the influence this prior trust exerts. Specifically, we investigate the effects of artificially stimulated prior trust as it transfers across disparate contexts and whether this prior trust can compensate for negative objective information. To study such incidents,...
The explosion and newly found reliance on open media has enabled rapid situational assessment especially in austere, hard to reach environments (e.g., sites with natural disasters, places of political unrest). However, the data, while plentiful has left users wishing to turn it into insights or information with a key void: trust assessment. Herein lies the challenge the authors aim to address in this...
This work provides a game theoretic framework through which one can study the different trust and mitigation strategies a decision maker can employ when soliciting advice or input from a potentially self-interested third-party. The framework supports a single decision maker's interacting with an arbitrary number of either honest or malicious (and malicious in varying ways) advisors. We include some...
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