Comparative Analysis of Statistical Model Checking Tools

Statistical model checking is a powerful and flexible approach for formal verification of computational models like P systems, which can have very large search spaces. Various statistical model checking tools have been developed, but choosing between them and using the most appropriate one requires a significant degree of experience, not only because different tools have different modelling and property specification languages, but also because they may be designed to support only a certain subset of property types. Furthermore, their performance can vary depending on the property types and membrane systems being verified. In this paper we evaluate the performance of various common statistical model checkers against a pool of biological models. Our aim is to help users select the most suitable SMC tools from among the available options, by comparing their modelling and property specification languages, capabilities and performances

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 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

Automatic Selection of Verification Tools for Efficient Analysis of Biochemical Models

YesMotivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this paper is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the “usability barrier” enabling biologists to have access to this powerful computational technology.EPSRC, Innovate U

Race and religion in online abuse towards UK politicians : working paper

Against a backdrop of tensions related to EU membership, we find levels of online abuse toward UK MPs reach a new high. Race and religion have become pressing topics globally, and in the UK this interacts with "Brexit" and the rise of social media to create a complex social climate in which much can be learned about evolving attitudes. In 8 million tweets by and to UK MPs in the first half of 2019, religious intolerance scandals in the UK's two main political parties attracted significant attention. Furthermore, high profile ethnic minority MPs started conversations on Twitter about race and religion, the responses to which provide a valuable source of insight. We found a significant presence for disturbing racial and religious abuse. We also explore metrics relating to abuse patterns, which may affect its impact. We find "burstiness" of abuse doesn't depend on race or gender, but individual factors may lead to politicians having very different experiences online

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. In this dataset, 480,000 users have been classified according to their “Brexit” vote intent. Findings include that linked material on Twitter was mostly posted by those in favour of leaving the EU. Mainstream news media had the greatest impact in terms of number of links tweeted, with alternative media and campaign sites appearing to a much lesser extent. Of the 15 most linked mainstream media, half show a substantially greater appeal to the leave camp, with two of them very much so. No mainstream media had a consistent appeal among remain supporters. Among the sites that were highly favoured by one voter valence or the other, the leave sites had by far the greatest impact in terms of number of appearances in tweets. Remain-preferred sites were less linked, and dominated by explicit campaign sites. Leave-preferred sites were more numerously linked, and dominated by mainstream and alternative media

Online abuse of UK MPs from 2015 to 2019: Working paper

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

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

Local media and geo-situated responses to Brexit: a quantitative analysis of Twitter, news and survey data

Societal debates and political outcomes are subject to news and social media influences, which are in turn subject to commercial and other forces. Local press are in decline, creating a "news gap". Research shows a contrary relationship between UK regions' economic dependence on EU membership and their voting in the 2016 UK EU membership referendum, raising questions about local awareness. We draw on a corpus of Twitter data which has been annotated for user location and Brexit vote intent, allowing us to investigate how location, topics of concern and Brexit stance are related. We compare this with a large corpus of articles from local and national news outlets, as well as survey data, finding evidence of a distinctly different focus in local reporting. National press focused more on terrorism and immigration than local press in most areas. Some Twitter users focused on immigration. Local press focused on trade, unemployment, local politics and agriculture. We find that remain voters shared interests more in keeping with local press on a per-region basis

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting