Examining Twitter Content of State Emergency Management During Hurricane Joaquin

Those tasked with disseminating life-protecting messages during crises have many factors to consider. Social media sites have become an information source for individuals during these times, and more research is needed examining the use of specific message strategies by emergency management agencies that may elicit attention and retransmission. This study examines Twitter content concerning Hurricane Joaquin. Content analysis of tweets from state emergency management accounts was performed to provide an overview of the content and stylistic elements used in tweets associated with the event. The findings are discussed in the context of both past research on the matter and implications for emergency management agencies responding to high-consequence events

Do tabloids poison the well of social media? Explaining democratically dysfunctional news sharing

This paper was accepted for publication in the journal New Media and Society and the definitive published version is available at https://doi.org/10.1177/1461444818769689The use of social media for sharing political information and the status of news as an essential raw material for good citizenship are both generating increasing public concern. We add to the debates about misinformation, disinformation, and “fake news” using a new theoretical framework and a unique research design integrating survey data and analysis of observed news sharing behaviors on social media. Using a media-as-resources perspective, we theorize that there are elective affinities between tabloid news and misinformation and disinformation behaviors on social media. Integrating four data sets we constructed during the 2017 UK election campaign—individual-level data on news sharing (N = 1,525,748 tweets), website data (N = 17,989 web domains), news article data (N = 641 articles), and data from a custom survey of Twitter users (N = 1313 respondents)—we find that sharing tabloid news on social media is a significant predictor of democratically dysfunctional misinformation and disinformation behaviors. We explain the consequences of this finding for the civic culture of social media and the direction of future scholarship on fake news

The Clickwrap: A Political Economic Mechanism for Manufacturing Consent on Social Media

The clickwrap is a digital prompt that facilitates consent processes by affording users the opportunity to quickly accept or reject digital media policies. A qualitative survey analysis was conducted ( N  = 513), assessing user interactions with the consent materials of a fictitious social media service, NameDrop. Findings suggest that clickwraps serve a political economic function by facilitating the circumvention of consent materials. Herman and Chomsky’s notion of the “buying mood” guides the analysis to analogize how social media maintain flow to monetized sections of services while diverting attention from policies that might encourage dissent. Clickwraps accomplish this through an agenda-setting function whereby prompts encouraging circumvention are made more prominent than policy links. Results emphasize that clickwraps discourage engagement with privacy and reputation protections by suggesting that consent materials are unimportant, contributing to the normalization of this circumvention. The assertion that clickwraps serve a political economic function suggests that capitalist methods of production are successfully being integrated into social media services and have the ability to manufacture consent

F-SED: Feature-Centric Social Event Detection

International audienceIn the context of social media, existent works offer social-event-based organization of multimedia objects (e.g., photos, videos) by mainly considering spatio-temporal data, while neglecting other user-related information (e.g., people, user interests). In this paper we propose an automated, extensible, and incremental Feature-centric Social Event Detection (F-SED) approach, based on Formal Concept Analysis (FCA), to organize shared multimedia objects on social media platforms and sharing applications. F-SED simultaneously considers various event features (e.g., temporal, geographical, social (user related)), and uses the latter to detect different feature-centric events (e.g., user-centric, location-centric). Our experimental results show that detection accuracy is improved when, besides spatio-temporal information, other features, such as social, are considered. We also show that the performance of our prototype is quasi-linear in most cases