Titlebook: Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining; Nitin Agarwal,Nima Dokoohaki,Serpil To

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Book 2019rstanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, be

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Studying Fake News via Network Analysis: Detection and Mitigation i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types, and propose how these networks can be used to detect and mitigate fake news on social media.

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Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Modelse for large networks we propose two new sampling schemes to approximate the distribution. Finally, we perform some experiments comparing a set of datasets with respect to their pattern distributions and comparing the fit of some random graph models to the datasets.

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Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analyticsial media data, they are insufficient to identify and characterize social influence bots, the networks in which they reside and their behavior. However, these bots can be identified, their prevalence assessed, and their impact on groups assessed using high dimensional network analytics. This is illu

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Studying Fake News via Network Analysis: Detection and Mitigationo enables the wide propagation of “fake news,” i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Identifying and mitigating fake news also presents unique challenges. To tackle these challenges, many existing research efforts e