Online attention for interpretable conflict estimation in political debates

Conflict arises naturally in dyadic interactions when involved individuals act on incompatible goals, interests, or actions. In this paper, the problem of conflict intensity estimation from audiovisual recordings is addressed. To this end, we propose an online attention-based neural network in order to learn a mapping from a sequence of audiovisual features to time-series describing conflict intensity. The proposed method is evaluated by conducting experiments in conflict intensity estimation by employing the CONFER dataset. Experimental results indicate the superiority of the proposed model compared to the state of the art. Furthermore, we demonstrate that by incorporating sparsity in the model, the origin of conflict can be traced back to specific key frames facilitating the interpretation of conflict escalation

Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods

Automated decisions are increasingly part of everyday life, but how can the public scrutinize, understand, and govern them? To begin to explore this, Omidyar Network has, in partnership with Upturn, published Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods.The report is based on an extensive review of computer and social science literature, a broad array of real-world attempts to study automated systems, and dozens of conversations with global digital rights advocates, regulators, technologists, and industry representatives. It maps out the landscape of public scrutiny of automated decision-making, both in terms of what civil society was or was not doing in this nascent sector and what laws and regulations were or were not in place to help regulate it.Our aim in exploring this is three-fold:1) We hope it will help civil society actors consider how much they have to gain in empowering the public to effectively scrutinize, understand, and help govern automated decisions; 2) We think it can start laying a policy framework for this governance, adding to the growing literature on the social and economic impact of such decisions; and3) We're optimistic that the report's findings and analysis will inform other funders' decisions in this important and growing field

Credibility analysis of textual claims with explainable evidence

Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.Das Web ist eine riesige Quelle wertvoller Informationen, allerdings wurde es durch die Verbreitung von Falschmeldungen verschmutzt. Eine zunehmende Anzahl an Hoaxes, Falschmeldungen und irreführenden Informationen im Internet haben viele Websites hervorgebracht, auf denen die Fakten überprüft und zweifelhafte Behauptungen manuell bewertet werden. Die rasante Verbreitung großer Mengen von Fehlinformationen sind jedoch zum Engpass für die manuelle Überprüfung geworden. Dies erfordert Tools zur Bewertung der Glaubwürdigkeit, mit denen dieser Überprüfungsprozess automatisiert werden kann. In früheren Arbeiten in diesem Bereich werden starke Annahmen gemacht über die Struktur der Behauptungen und die Portale, in denen sie gepostet werden. Vor allem aber können die Black-Box-Techniken, die in früheren Arbeiten vorgeschlagen wurden, nicht erklären, warum eine bestimmte Aussage als glaubwürdig erachtet wird oder nicht. Um diesen Einschränkungen zu begegnen, wird in dieser Dissertation ein allgemeines Framework für die automatisierte Bewertung der Glaubwürdigkeit vorgeschlagen, bei dem keine Annahmen über die Struktur oder den Ursprung der Behauptungen gemacht werden. Insbesondere schlagen wir ein featurebasiertes Modell vor, das automatisch relevante Artikel zu einer bestimmten Behauptung abruft und deren Glaubwürdigkeit bewertet, indem die gegenseitige Interaktion zwischen dem Sprachstil der relevanten Artikel, ihre Haltung zur Behauptung und der Vertrauenswürdigkeit der zugrunde liegenden Quellen erfasst wird. Wir verbessern unseren Ansatz zur Bewertung der Glaubwürdigkeit weiter und schlagen ein auf neuronalen Netzen basierendes Modell vor. Im Gegensatz zum featurebasierten Modell ist dieses Modell nicht auf Feature-Engineering und externe Lexika angewiesen. Unsere beiden Modelle machen ihre Einschätzungen interpretierbar, indem sie erklärbare Beweise aus sorgfältig ausgewählten Webquellen extrahieren. Wir verwenden unsere Modelle zur Entwicklung eines Webinterfaces, CredEye, mit dem Benutzer die Glaubwürdigkeit einer Behauptung in Textform automatisch bewerten und verstehen können, indem sie automatisch ausgewählte Beweisstücke einsehen. Darüber hinaus untersuchen wir das Problem der Positionsklassifizierung und schlagen ein auf neuronalen Netzen basierendes Modell vor, um die Position verschiedener Benutzerperspektiven in Bezug auf die umstrittenen Behauptungen vorherzusagen. Bei einer kontroversen Behauptung und einem Benutzerkommentar sagt unser Einstufungsmodell voraus, ob der Benutzerkommentar die Behauptung unterstützt oder ablehnt

A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 ( n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research

Undermining, defusing or defending European integration? Assessing public communication of European executives in times of EU politicisation

How do mainstream political executives cue their politicised constituencies on European integration? Moving beyond static expectations that EU politicisation induces executives to either undermine, defuse or defend integration, this article theorises executives’ incentives under different configurations of public and partisan Euroscepticism in their home countries. Expectations are tested on the sentiment and complexity that executives attach to European integration in almost 9,000 public speeches delivered throughout the Euro Crisis. It is found that national leaders faced with sceptical public opinion and low levels of partisan Euroscepticism rhetorically undermine integration, whereas European Commissioners faced with similar conditions are prone to defend it. These responses intensify disproportionally with growing public Euroscepticism, but are moderated by Eurosceptic party strength in surprising ways. When such challenger parties come closer to absorbing the Eurosceptic potential in public opinion, executive communication turns more positive again but also involves less clear rhetorical signals. These findings move beyond existing uniform expectations on mainstream responses to Eurosceptic challenges and highlight the relevance of different domestic configurations of EU politicisation

Essays in political text: new actors, new data, new challenges

The essays in this thesis explore diverse manifestations and different aspects of political text. The two main contributions on the methodological side are bringing forward novel data on political actors who were overlooked by the existing literature and application of new approaches in text analysis to address substantive questions about them. On the theoretical side this thesis contributes to the literatures on lobbying, government transparency, post-conflict studies and gender in politics. In the first paper on interest groups in the UK I argue that contrary to much of the theoretical and empirical literature mechanisms of attaining access to government in pluralist systems critically depend on the presence of limits on campaign spending. When such limits exist, political candidates invest few resources in fund-raising and, thus, most organizations make only very few if any political donations. I collect and analyse transparency data on government department meetings and show that economic importance is one of the mechanisms that can explain variation in the level of access attained by different groups. Furthermore, I show that Brexit had a diminishing effect on this relationship between economic importance and the level of access. I also study the reported purpose of meetings and, using dynamic topic models, show the temporary shifts in policy agenda during this period. The second paper argues that civil society in post-conflict settings is capable of high-quality deliberation and, while differing in their focus, both male and female can deliver arguments pertaining to the interests of broader societal groups. Using the transcripts of civil society public consultation meetings across former Yugoslavia I show that the lack of gender-sensitive transitional justice instruments could stem not from the lack of women’s 3 physical or verbal participation, but from the dynamic of speech enclaves and topical focus on different aspects of transitional justice process between genders. And, finally, the third paper maps the challenges that lie ahead with the proliferation of research that relies on multiple datasets. In a simulation study I show that, when the linking information is limited to text, the noise can potential occur at different levels and is often hard to anticipate in practice. Thus, the choice of record linkage requires balancing between these different scenarios. Taken together, the papers in this thesis advance the field of “text as data” and contribute to our understanding of multiple political phenomena

Legal ideology, legal doctrine and the UK's top judges

Most work on the UK's judiciary reflects the assumption that the institutional issues raised by attitudinal studies of the US Supreme Court are irrelevant to the UK because the UK's judiciary is not political. This article challenges those assumptions. We present an empirical and theoretical analysis of the 'doctrinal model' of judicial decision-making in the upper judiciary of the UK, that is to say, of the position that judges decide cases on the basis of doctrinal positions rather than political views, and argue that it has far more in common with the attitudinal model than is conventionally assumed. We elaborate upon this through an empirical analysis of decisions of the Law Lords on challenges to state bodies over a twenty-five year period, which estimates judges' ideological positions on a scale derived from doctrine. We find that (a) there are meaningful and measurable differences in judicial positions in key doctrinal controversies (b) these differences have an impact on the outcome of a significant minority of cases. Our results support the view that doctrinal positions are more salient than party-political ideology in the UK context, but also demonstrate that even faithful adherence to a doctrinal model does not affect the validity of the insights of the attitudinal model in relation to the role and impact of judges' personal views. We show that on a proper understanding, doctrinal adjudication raises the same questions of institutional structure and design emphasised by the attitudinal model, and that these questions assume particular significance given changes to the British judiciary's institutional role