A selection of our most recent and relevant publications is listed below. For the full list of GATE publications, please visit the publications page on the main GATE website.

20th March 2019

A Framework for Real-Time Semantic Social Media Analysis

This paper presents a framework for collecting and analysing large volume social media content. The real-time analytics framework comprises semantic annotation, Linked Open Data, semantic search, and dynamic result aggregation components. In addition, exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices, term clouds, treemaps, and choropleths. There is also an interactive semantic search interface (Prospector), where users can save, refine, and analyse the results of semantic search queries over time.

20th March 2019

Quantifying Media Influence and Partisan Attention on Twitter during the UK EU Referendum

User generated media, and their influence on the information individuals are exposed to, have the potential to affect political outcomes. This is increasingly a focus for attention and concern. The British EU membership referendum provided an opportunity for researchers to explore the nature and impact of the new infosphere in a politically charged situation. This work contributes by reviewing websites that were linked in a Brexit Tweet dataset of 13.2 million tweets, by 1.8 million distinct users, collected in the run-up to the referendum.

Research materials relating to the work can be found here.

30th July 2019

Twits, Twats and Twaddle: Trends in Online Abuse towards UK Politicians

Concerns have reached the mainstream about how social media are affecting political outcomes. One trajectory for this is the exposure of politicians to online abuse. In this paper we use 1.4 million tweets from the months before the 2015 and 2017 UK general elections to explore the abuse directed at politicians. Results show that abuse increased substantially in 2017 compared with 2015. Abusive tweets show a strong relationship with total tweets received, indicating for the most part impersonality, but a second pathway targets less prominent individuals, suggesting different kinds of abuse. Accounts that send abuse are more likely to be throwaway. Economy and immigration were major foci of abusive tweets in 2015, whereas terrorism came to the fore in 2017.

The gazetteer of abusive terms used in the work is available here.

31st July 2019

Partisanship, Propaganda and Post-Truth Politics: Quantifying Impact in Online Debate

The recent past has highlighted the influential role of social networks and online media in shaping public debate on current affairs and political issues. This paper is focused on studying the role of politically-motivated actors and their strategies for influencing and manipulating public opinion online: partisan media, state-backed propaganda, and post-truth politics. In particular, we present quantitative research on the presence and impact of these three “Ps” in online Twitter debates in two contexts: (i) the run up to the UK EU membership referendum (“Brexit”); and (ii) the information operations of Russia-backed online troll accounts. We first compare the impact of highly partisan versus mainstream media during the Brexit referendum, specifically comparing tweets by half a million “leave” and “remain” supporters. Next, online propaganda strategies are examined, specifically left- and right-wing troll accounts. Lastly, we study the impact of misleading claims made by the political leaders of the leave and remain campaigns. This is then compared to the impact of the Russia-backed partisan media and propaganda accounts during the referendum. In particular, just two of the many misleading claims made by politicians during the referendum were found to be cited in 4.6 times more tweets than the 7,103 tweets related to Russia Today and Sputnik and in 10.2 times more tweets than the 3,200 Brexit-related tweets by the Russian troll accounts.

Brexit experimental materials
Twitter elections integrity datasets

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