BackgroundIn 2014, the world was startled by a sudden outbreak of Ebola. Although Ebola infections and deaths occurred almost exclusively in Guinea, Sierra Leone, and Liberia, few potential Western cases, in particular, caused a great stir among the public in Western countries.ObjectiveThis study builds on the construal level theory to examine the relationship between psychological distance to an epidemic and public attention and sentiment expressed on Twitter. Whereas previous research has shown the potential of social media to assess real-time public opinion and sentiment, generalizable insights that further the theory development lack.MethodsEpidemiological data (number of Ebola infections and fatalities) and media data (tweet volume and key events reported in the media) were collected for the 2014 Ebola outbreak, and Twitter content from the Netherlands was coded for (1) expressions of fear for self or fear for others and (2) psychological distance of the outbreak to the tweet source. Longitudinal relations were compared using vector error correction model (VECM) methodology.ResultsAnalyses based on 4500 tweets revealed that increases in public attention to Ebola co-occurred with severe world events related to the epidemic, but not all severe events evoked fear. As hypothesized, Web-based public attention and expressions of fear responded mainly to the psychological distance of the epidemic. A chi-square test showed a significant positive relation between proximity and fear: χ22=103.2 (P<.001). Public attention and fear for self in the Netherlands showed peaks when Ebola became spatially closer by crossing the Mediterranean Sea and Atlantic Ocean. Fear for others was mostly predicted by the social distance to the affected parties.ConclusionsSpatial and social distance are important predictors of public attention to worldwide crisis such as epidemics. These factors need to be taken into account when communicating about human tragedies.

This article scrutinizes the method of automated content analysis to measure the tone of news coverage. We compare a range of off-the-shelf sentiment analysis tools to manually coded economic news as well as examine the agreement between these dictionary approaches themselves. We assess the performance of five off-the-shelf sentiment analysis tools and two tailor-made dictionary-based approaches. The analyses result in five conclusions. First, there is little overlap between the off-the-shelf tools; causing wide divergence in terms of tone measurement. Second, there is no stronger overlap with manual coding for short texts (i.e., headlines) than for long texts (i.e., full articles). Third, an approach that combines individual dictionaries achieves a comparably good performance. Fourth, precision may increase to acceptable levels at higher levels of granularity. Fifth, performance of dictionary approaches depends more on the number of relevant keywords in the dictionary than on the number of valenced words as such; a small tailor-made lexicon was not inferior to large established dictionaries. Altogether, we conclude that off-the-shelf sentiment analysis tools are mostly unreliable and unsuitable for research purposesat least in the context of Dutch economic newsand manual validation for the specific language, domain, and genre of the research project at hand is always warranted.

Social media are becoming increasingly important for communication between government organisations and citizens. Although research on this issue is expanding, the structure of these new communication patterns is still poorly understood. This study contributes to our understanding of these new communication patterns by developing an explanatory model of message diffusion on social media. Messages from 964 Dutch police force Twitter accounts are analysed using trace data drawn from the Twitter™ API to explain why certain police tweets are forwarded and others are not. Based on an iterative human calibration procedure, message topics were automatically coded based on customised lexicons. A principal component analysis of message characteristics generated four distinct patterns of use in (in)personal communication and new/versus reproduced content. Message characteristics were combined with user characteristics in a multilevel logistic general linear model. Our main results show that URLs or use of informal communication increases chances of message forwarding. In addition, contextual factors such as user characteristics impact diffusion probability. Recommendations are discussed for further research into authorship styles and their implications for social media message diffusion. For the police and other government practitioners, a list of recommendation about how to reach a larger number of citizens through social media communications is presented.

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While data-driven personalization strategies are permeating all areas of online communication, the impact for individuals and society as a whole is still not fully understood. Drawing on Facebook as a case study, we combine online tracking and self-reported survey data to assess who gets targeted with what content. We tested relationships between user characteristics (i.e. socio-demographic and individual perceptions) and exposure to branded content on Facebook. Findings suggest that social media use sophisticated algorithms to target specific groups of users, especially in the context of gender-stereotyping and health. Health-related content was predominantly targeted at older users, females, and at those with higher levels of trust in online companies, as well as those in poorer health conditions. This study provides a first indication of unfair targeting that reinforces stereotypes and creates inequalities, and suggests rethinking the impact of algorithmic targeting in creating new forms of individual and societal vulnerabilities.