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Abusive Democracy: The Trolling of UK MPs on Twitter

Social Media
Stephen Ward
University of Salford
Stephen Ward
University of Salford
Liam McLoughlin
University of Salford

Internet communication was initially seen as a tool for supporting conversational dialogue between MPs and the public, potentially helping to create a thicker form of representation (Coleman, 2005). The rise of social media, however, has led to a series of mainly anecdotal reports via the media of the widespread (racist, misogynistic, ideologically-driven) abuse of MPs online (Guardian, 16 June 2016). Whilst there has always been an element of physical-risk to elected representatives (Every-Palmer et al, 2015), the implication of these reports is that abuse has become systematic and driven by anonymity/ease of use of social media. As yet, though, there is only limited academic evidence of both the scale and nature of the threat. Some initial studies have suggested that online trolling of MPs is largely a means to exonerate boredom and attract attention, rather than any serious threat to endanger individuals (Buckels, et al, 2014; Shachaf & Hara, 2010). This paper, therefore, seeks to create an understanding of the relationship between online trolling and UK MPs by exploring the following questions:
• Scale - what quantifiable measurement of abusive messages can we find to MPs online?
• Nature – Can we identify prominent types of abuse or is it more generalised/non-specific?
• Targets - Do particular demographics of MPs, such as female representatives or from particular ideological groups, attract more online abuse?
• Profile - Are there particular “types” of online abusers & do they seek to attack one MP or are they serial offenders attacking multiple representatives?
To answer the questions above we analysed a database of tweets sent to 571MPs compiled from a two-month collection period. This data was then filtered using a corpus to identify messages with offensive or threating content. The abusive tweets were then studied through social media and content analysis methodologies to identify trends within the data.
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