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Abstract

Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text-based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine-learning techniques. The experimental results indicate that a multimodal approach outperforms single-modality approaches, allowing for better fake news detection.

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Abstract

The popularity of location-based features in social networks has been increasing over the past few years. Location information gathered from social networks can threaten users' information privacy through granular tracking and exposure of their preferences, behaviors, and identity. In this 6-week study (N = 35), we investigate the effect of "audience-group"-based interventions on Facebook check-in behavior of participants. These "audience-group"-based nudges help close the gap between the users' perceived audiences and those that are permitted to view their check-ins. The nudges remind users that their real-time location information may be visible to a larger group of friends than they expect. Based on both quantitative and qualitative data analyses, we report that reminding users of the unexpected audiences that have access to their location check-ins could be a promising way to help users manage their privacy in online location sharing. These findings motivate several recommendations for app designers as well as information privacy researchers to better design and evaluate location sharing in online social networks.