This article seeks to explain variation in news sharing patterns on social media. It finds that news editors have considerable power to shape the social media agenda through the use of “story importance cues” but also shows that there are some areas of news reporting (such as those related to crime and disasters) where this power does not apply. This highlights the existence of a social “news gap” where social media filters out certain types of news, producing a social media news agenda which has important differences from its traditional counterpart. The discussion suggests that this may be consequential for perceptions of crime and engagement with politics; it might even stimulate a partial reversal of the tabloidization of news outlets.

This article is a systematic large-scale study of the reasons driving political fragmentation on social media. Making use of a comparative dataset of the Twitter discussion activities of 115 political groups in 26 countries, it shows that groups that are further apart in ideological terms interact less, and that groups that sit at the extremes of the ideological scale are particularly likely to have lower patterns of interaction. Indeed, exchanges between centrists who sit on different sides of the left-right divide are more likely than connections between centrists and extremists who are from the same ideological wing. In light of the results, theory about exposure to different ideological viewpoints online is enhanced.

She studies datarelated work practices in multi-stakeholder environments and explores how data may become a design medium.

Social media are now a routine part of political campaigns all over the world. However, studies of the impact of campaigning on social platform have thus far been limited to cross-section datasets from one election period which are vulnerable to unobserved variable bias. Hence empirical evidence on the effectiveness of political social media activity is thin. We address this deficit by analysing a novel panel dataset of political Twitter activity in the 2015 and 2017 elections in the United Kingdom. We find that Twitter based campaigning does seem to help win votes, a finding which is consistent across a variety of different model specifications including a first difference regression. The impact of Twitter use is small in absolute terms, though comparable with that of campaign spending. Our data also support the idea that effects are mediated through other communication channels, hence challenging the relevance of engaging in an interactive fashion.

The emergence of large stores of transactional data generated by increasing use of digital devices presents a huge opportunity for policymakers to improve their knowledge of the local environment and thus make more informed and better decisions. A research frontier is hence emerging which involves exploring the type of measures that can be drawn from data stores such as mobile phone logs, Internet searches and contributions to social media platforms and the extent to which these measures are accurate reflections of the wider population. This paper contributes to this research frontier, by exploring the extent to which local commuting patterns can be estimated from data drawn from Twitter. It makes three contributions in particular. First, it shows that heuristics applied to geolocated Twitter data offer a good proxy for local commuting patterns; one which outperforms the current best method for estimating these patterns (the radiation model). This finding is of particular significance because we make use of relatively coarse geolocation data (at the city level) and use simple heuristics based on frequency counts. Second, it investigates sources of error in the proxy measure, showing that the model performs better on short trips with higher volumes of commuters; it also looks at demographic biases but finds that, surprisingly, measurements are not significantly affected by the fact that the demographic makeup of Twitter users differs significantly from the population as a whole. Finally, it looks at potential ways of going beyond simple frequency heuristics by incorporating temporal information into models.