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This study investigates cross-platform differences in social media by analyzing the contending candidates who represent different political ideology during the 2016 presidential election. Borrowing the frame-building and frame-effect perspectives, it examines the ways in which the two contending candidates (Donald Trump and Hillary Clinton) built their message frames in two different social platforms-Twitter (N = 3,805) and Facebook (N = 655)-and how the frame differences affected audience engagement in each platform. The results showed that Trump's messages presented more variety in frame selection than Clinton's, focusing on conflict and negative emotional frames on Twitter while displaying frequent positive emotional frames on Facebook. Clinton's strategy relied heavily on conflict and positive emotional frames on both Twitter and Facebook. The results also suggested that for both Trump and Clinton followers on Twitter, conflict and morality frames consistently attracted retweeting behaviors and emotional frames attracted favoriting behaviors. However, Facebook engagement behaviors did not show a consistent pattern between the followers of the two candidates.

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This study examines the dark information commons in Reddit, a pseudonymous social news site, in the context of illicit e-commerce communities. Unlike market forums that are hosted in the dark web, Reddit is open to users of both the clear and dark web, unwittingly serving as a gateway for potential newcomers to learn and prepare their entrance into darknet market systems. To illustrate how information commons in Reddit bolster the resilience of illicit e-commerce communication ecology, this study presents information and knowledge flows in a subreddit community dedicated to a now-defunct market called AlphaBay. This study expands the discussion of the challenges that Reddit, as information commons, encounters in governing the flow of dark knowledge.

From requesting Alexa to set a reminder to asking Google Assistant to make a call, artificial intelligence (AI)‐enabled voice assistants are quickly melding into our lives. This study aims to understand why users interact with a voice assistant system. Results from an online survey identified four types of motivations underlying the use of voice assistants: entertainment, companionship, dynamic control, and functional utility. Results showed that functional utility and dynamic control were positively related to users' satisfaction, while companionship and entertainment were not. The effect of social presence on users' satisfaction was also explored. The moderation analyses showed that social presence not only had a main effect but also played a significant role in increasing satisfaction among the users who perceived low levels of functional utility and dynamic control. This study advances a growing body of human–AI interaction literature by demonstrating the underlying mechanism behind voice assistants' use. Practical and theoretical implications are also discussed.