Vindication, virtue and vitriol: A study of online engagement and abuse toward British MPs during the COVID-19 pandemic

COVID-19 has given rise to a lot of malicious content online, including hate speech, online abuse, and misinformation. British MPs have also received abuse and hate on social media during this time. To understand and contextualise the level of abuse MPs receive, we consider how ministers use social media to communicate about the pandemic, and the citizen engagement that this generates. The focus of the paper is on a large-scale, mixed-methods study of abusive and antagonistic responses to UK politicians on Twitter, during the pandemic from early February to late May 2020. We find that pressing subjects such as financial concerns attract high levels of engagement, but not necessarily abusive dialogue. Rather, criticising authorities appears to attract higher levels of abuse during this period of the pandemic. In addition, communicating about subjects like racism and inequality may result in accusations of virtue signalling or pandering by some users. This work contributes to the wider understanding of abusive language online, in particular that which is directed at public officials

Social media and information overload : survey results

A UK-based online questionnaire investigating aspects of usage of user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy

Bio-YODIE : a named entity linking system for biomedical text

Ever-expanding volumes of biomedical text require automated semantic annotation techniques to curate and put to best use. An established field of research seeks to link mentions in text to knowledge bases such as those included in the UMLS (Unified Medical Language System), in order to enable a more sophisticated understanding. This work has yielded good results for tasks such as curating literature, but increasingly, annotation systems are more broadly applied. Medical vocabularies are expanding in size, and with them the extent of term ambiguity. Document collections are increasing in size and complexity, creating a greater need for speed and robustness. Furthermore, as the technologies are turned to new tasks, requirements change; for example greater coverage of expressions may be required in order to annotate patient records, and greater accuracy may be needed for applications that affect patients. This places new demands on the approaches currently in use. In this work, we present a new system, Bio-YODIE, and compare it to two other popular systems in order to give guidance about suitable approaches in different scenarios and how systems might be designed to accommodate future needs

Which politicians receive abuse? Four factors illuminated in the UK general election 2019

The 2019 UK general election took place against a background of rising online hostility levels toward politicians, and concerns about the impact of this on democracy, as a record number of politicians cited the abuse they had been receiving as a reason for not standing for re-election. We present a four-factor framework in understanding who receives online abuse and why. The four factors are prominence, events, online engagement and personal characteristics. We collected 4.2 million tweets sent to or from election candidates in the six week period spanning from the start of November until shortly after the December 12th election. We found abuse in 4.46% of replies received by candidates, up from 3.27% in the matching period for the 2017 UK general election. Abuse levels have also been climbing month on month throughout 2019. Abuse also escalated throughout the campaign period. Abuse focused mainly on a small number of high profile politicians, with the most prominent individuals receiving not only more abuse by volume, but also as a percentage of replies. Abuse is ``spiky'', triggered by external events such as debates, or certain tweets. Some tweets may become viral targets for personal abuse. On average, men received more general and political abuse; women received more sexist abuse. Conservative candidates received more political and general abuse. We find that individuals choosing not to stand for re-election had received more abuse across the preceding year

Online Abuse of UK MPs in 2015 and 2017: Perpetrators, Targets, and Topics

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. This collection allows us to look at abuse broken down by both party and gender and aimed at specific Members of Parliament. It also allows us to investigate the characteristics of those who send abuse and their topics of interest. Results show that in both absolute and proportional terms, abuse increased substantially in 2017 compared with 2015. Abusive replies are somewhat less directed at women and those not in the currently governing party. Those who send the abuse may be issue-focused, or they may repeatedly target an individual. In the latter category, accounts are more likely to be throwaway. Those sending abuse have a wide range of topical triggers, including borders and terrorism

Online abuse toward candidates during the UK general election 2019 : working paper

The 2019 UK general election took place against a background of rising online hostility levels toward politicians and concerns about its impact on democracy. We collected 4.2 million tweets sent to or from election candidates in the six week period spanning from the start of November until shortly after the December 12th election. We found abuse in 4.46\% of replies received by candidates, up from 3.27\% in the matching period for the 2017 UK general election. Abuse levels have also been climbing month on month throughout 2019. Abuse also escalated throughout the campaign period. Abuse focused mainly on a small number of high profile politicians. Abuse is "spiky", triggered by external events such as debates, or certain tweets. Abuse increases when politicians discuss inflammatory topics such as borders and immigration. There may also be a backlash on topics such as social justice. Some tweets may become viral targets for personal abuse. On average, men received more general and political abuse; women received more sexist abuse. MPs choosing not to stand again had received more abuse during 2019

MP Twitter abuse in the age of COVID-19 : white paper

As COVID-19 sweeps the globe, outcomes depend on effective relationships between the public and decision-makers. In the UK there were uncivil tweets to MPs about perceived UK tardiness to go into lockdown. The pandemic has led to increased attention on ministers with a role in the crisis. However, generally this surge has been civil. Prime minister Boris Johnson's severe illness with COVID-19 resulted in an unusual peak of supportive responses on Twitter. Those who receive more COVID-19 mentions in their replies tend to receive less abuse (significant negative correlation). Following Mr Johnson's recovery, with rising economic concerns and anger about lockdown violations by influential figures, abuse levels began to rise in May. 1,902 replies to MPs within the study period were found containing hashtags or terms that refute the existence of the virus (e.g. #coronahoax, #coronabollocks, 0.04% of a total 4.7 million replies, or 9% of the number of mentions of "stay home save lives" and variants). These have tended to be more abusive. Evidence of some members of the public believing in COVID-19 conspiracy theories was also found. Higher abuse levels were associated with hashtags blaming China for the pandemic

A third preliminary study of the true macular vision test

The purpose of this thesis is to make a comparison of the amount of anisometropia which was obtained by a septum technique at near utilizing alternate fixations with the standard 20/40 blur and equalization test at far

Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project.

OBJECTIVES: We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research. DESIGN: Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries. SETTING: Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK. PARTICIPANTS: The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis. OUTCOME MEASURES: Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database. RESULTS: We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis. CONCLUSIONS: This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups

Concept selection for advanced low-emission coal fired boiler

The Babcock & Wilcox Company (B&W), under contract to the US Department of Energy (DOE) with subcontract to Physical Sciences, Inc. (PSIT), the Massachusetts Institute of Technology (MIT) and United Engineers and Constructors (UE&C) has begun development of an advanced low-emission boiler system (LEBS). The initial phase of this multi-phase program required a thorough review and assessment of potential advanced technologies and techniques for control of combustion and flue gas emissions. Results of this assessment are presented in this paper