text: We extend previous work about general election-related abuse of UK MPs with two new time periods, one in late 2018 and the other in early 2019, allowing previous observations to be extended to new data and the impact of key stages in the UK withdrawal from the European Union on patterns of abuse to be explored. The topics that draw abuse evolve over the four time periods are reviewed, with topics relevant to the Brexit debate and campaign tone showing a varying pattern as events unfold, and a suggestion of a "bubble" of topics emphasized in the run-up to the highly Brexit-focused 2017 general election. Brexit stance shows a variable relationship with abuse received. We find, as previously, that in quantitative terms, Conservatives and male politicians receive more abuse. Gender difference remains significant even when accounting for prominence, as gauged from Google Trends data, but prominence, or other factors related to being in power, as well as gender, likely account for the difference associated with party membership. No clear relationship between ethnicity and abuse is found in what remains a very small sample (BAME and mixed heritage MPs). Differences are found in the choice of abuse terms levelled at female vs. male MPs.
  @cite_11: This paper investigates political homophily on Twitter. Using a combination of machine learning and social network analysis we classify users as Democrats or as Republicans based on the political content shared. We then investigate political homophily both in the network of reciprocated and nonreciprocated ties. We find that structures of political homophily differ strongly between Democrats and Republicans. In general, Democrats exhibit higher levels of political homophily. But Republicans who follow official Republican accounts exhibit higher levels of homophily than Democrats. In addition, levels of homophily are higher in the network of reciprocated followers than in the nonreciprocated network. We suggest that research on political homophily on the Internet should take the political culture and practices of users seriously.
  @cite_4: ABSTRACTWith the advances in interpersonal communication of the “Web 2.0” era, questions about the importance of civility are perhaps more important than ever. Mass digital interaction between strangers has become an everyday occurrence, bound by few behavioral norms. I argue that the widespread presence of incivility in online political communication limits the deliberative potential of online interactions. To test this hypothesis, I manipulate exposure to uncivil political discourse in an online discussion forum. I find that exposure to disagreeable uncivil political talk induces feelings of anger and aversion, which in turn reduces satisfaction with the message board discourse. On the other hand, exposure to like-minded incivility increases the use of uncivil behavior in political comments by message board posters. Notably, these effects mainly occur when histrionic, emotional incivility is present. I discuss why like-minded and disagreeable incivility have different effects, and reflect on what the pr...
  @cite_33: Existing studies focusing on politicians' adoption of Twitter have found that they use it primarily as a broadcasting tool. We argue that citizens' impolite and or uncivil behavior is one possible explanation for such decisions. Social media conversations are rife with harassment and politicians are a prime target. This alters the incentive structure of engaging in dialogue on social media. We use Spanish, Greek, German, and U.K. candidates' tweets sent during the run-up to the recent European Parliament elections, and rely on automated text analysis and machine learning methods to measure their level of civility. Our contribution is an actor-oriented theory of political dialogue that incorporates Twitter's specific affordances, clarifying how and why Twitter's democratic promise may be limited.
  @cite_3: 
  @cite_19: This paper analyses changes in public opinion by tracking political discussions in which people voluntarily engage online. Unlike polls or surveys, our approach does not elicit opinions but approximates what the public thinks by analysing the discussions in which they decide to take part. We measure the emotional content of online discussions in three dimensions (valence, arousal and dominance), paying special attention to deviation around average values, which we use as a proxy for disagreement and polarisation. We show that this measurement of public opinion helps predict presidential approval rates, suggesting that there is a point of connection between online discussions (often deemed not representative of the overall population) and offline polls. We also show that this measurement provides a deeper understanding of the individual mechanisms that drive aggregated shifts in public opinion. Our data spans a period that includes two US presidential elections, the attacks of September 11, and the start of military action in Afghanistan and Iraq.
  @cite_5: As more and more people use social media to communicate their view and perception of elections, researchers have increasingly been collecting and analyzing data from social media platforms. Our research focuses on social media communication related to the 2013 election of the German parliament [translation: Bundestagswahl 2013]. We constructed several social media datasets using data from Facebook and Twitter. First, we identified the most relevant candidates (n=2,346) and checked whether they maintained social media accounts. The Facebook data was collected in November 2013 for the period of January 2009 to October 2013. On Facebook we identified 1,408 Facebook walls containing approximately 469,000 posts. Twitter data was collected between June and December 2013 finishing with the constitution of the government. On Twitter we identified 1,009 candidates and 76 other agents, for example, journalists. We estimated the number of relevant tweets to exceed eight million for the period from July 27 to September 27 alone. In this document we summarize past research in the literature, discuss possibilities for research with our data set, explain the data collection procedures, and provide a description of the data and a discussion of issues for archiving and dissemination of social media data.
  @cite_10: Using 414,322 tweets drawn from 143,404 individual Twitter users located in all 435 U.S. congressional districts, this study employed big data and automated content analysis techniques to explore the degree to which socioeconomic status SES, social capital potential the degree to which a congressional district has the potential for interconnected citizen networks, and in-district partisan polarization were associated with incivility on Twitter during the 2012 presidential election. Broadly speaking, and with some exceptions, the results indicated that election oriented incivility on Twitter was highest in districts that had low SES indicators, low levels of social capital potential, and low levels of partisan polarity. In its sum, this study shows how large social data sets i.e., the Census can be combined with big data to explain social phenomena.
  @cite_25: In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.
  @cite_20: Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.
  @cite_17: We present a novel approach to using anonymized web search query logs to analyze and visualize political issues. Our starting point is a list of politically annotated blogs (left vs. right). We use this list to assign a numerical political leaning to queries leading to clicks on these blogs. Furthermore, we map queries to Wikipedia articles and to fact-checked statements from politifact.com, as well as applying sentiment analysis to search results. With this rich, multi-faceted data set we obtain novel graphical visualizations of issues and discover connections between the different variables. Our findings include (i) an interest in "the other side" where queries about Democrat politicians have a right leaning and vice versa, (ii) evidence that "lies are catchy" and that queries pertaining to false statements are more likely to attract large volumes, and (iii) the observation that the more right-leaning a query it is, the more negative sentiments can be found in its search results.
summary: Whilst online fora have attracted much attention as a way of exploring political dynamics @cite_20 @cite_5 @cite_11 @cite_17 @cite_25 @cite_19 , and the effect of abuse and incivility in these contexts has been explored @cite_10 @cite_33 @cite_4 @cite_3 , little work exists regarding the abusive and intimidating ways people address politicians online; a trend that has worrying implications for democracy. collected tweets centred around candidates for the European Parliament election in 2014 from Spain, Germany, the United Kingdom and France posted in the month surrounding the election. They find that the extent of the abuse and harrassment a politician is subject to correlates with their engagement with the medium. Ward and McLoughlin ward2017turds find similar results to ours, for example regarding the greater abuse received by male MPs, in a two and a half month period running from late 2016 to early 2017; we contribute with a more in-depth exploration of how prominence relates to abuse received, and by studying four time periods over five years. They consider hate speech gendered slurs separately, however, and find that women receive more of these. They find that most Twitter abuse takes the form of a reply.