Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Understanding the Roots of Radicalisation on Twitter

In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation

Regional Data

Spatiality is an increasingly important dimension in the social sciences, as a new wave of recent publications shows. Intra-national comparisons have proved to be as fruitful as the better known inter-national analysis. Regional characteristics are found to have considerable influence on individual behaviour. This movement was fostered by methodological advances, e.g. in multi-level techniques. On the data side spatial analysis is supported by a good basic infrastructure in official and semi-official information, often provided in the way of easily usable DVDs. In addition, both scientific researchers and commercial marketing firms collect valuable information, partly on a very detailed local level of only a handful of households. However, there is ample room for improvement. Huge existing datasets (e.g. PISA E) are not open for spatial oriented scientific purposes; in many cases regional information is not sufficiently available (e.g. on criminal behaviour); systematic over-sampling in sparsely inhabited areas to allow additional regional analysis is not very common.

Political, religious and occupational identities in context: Placing identity status paradigm in context

This study critically contrasts global identity with domain-specific identities (political, religious and occupational) and considers context and gender as integral parts of identity. In a cross-sectional survey, 1038 Greek Cypriot adolescents (449 boys and 589 girls, mean age 16.8) from the three different types of secondary schools (state, state technical and private) and from different SES completed part of the Extended Objective Measure of Ego-Identity Status-2 (EOMEIS-2). The macrocontext of Greek Cypriot society is used to understand the role of context in adolescents’ identities. Results showed that Greek Cypriot young people were not in the same statuses across their global, political, religious and occupational identities. This heterogeneity in the status of global identity and of each identity domain is partially explained by differences in gender, type of school and SES (Socio-Economic Status). The fact that identity status is found to be reactive to context suggests that developmental stage models of identity status should place greater emphasis on context

The impact of Facebook use on micro-level social capital: a synthesis

The relationship between Facebook use and micro-level social capital has received substantial scholarly attention over the past decade. This attention has resulted in a large body of empirical work that gives insight into the nature of Facebook as a social networking site and how it influences the social benefits that people gather from having social relationships. Although the extant research provides a solid basis for future research into this area, a number of issues remain underexplored. The aim of the current article is twofold. First, it seeks to synthesize what is already known about the relationship between Facebook use and micro-level social capital. Second, it seeks to advance future research by identifying and analyzing relevant theoretical, analytical and methodological issues. To address the first research aim, we first present an overview and analysis of current research findings on Facebook use and social capital, in which we focus on what we know about (1) the relationship between Facebook use in general and the different subtypes of social capital; (2) the relationships between different types of Facebook interactions and social capital; and (3) the impact of self-esteem on the relationship between Facebook use and social capital. Based on this analysis, we subsequently identify three theoretical issues, two analytical issues and four methodological issues in the extant body of research, and discuss the implications of these issues for Facebook and social capital researchers

A Multilevel Analysis of Implicit and Explicit CSR in French and UK Professional Sport

Research question: This paper examines the ways in which French and UK professional sports clubs implement and communicate their CSR policies. In addition to identifying similarities and differences between CSR practices in the two countries, our analysis extends and adapts the implicit-explicit CSR framework to the field of sport. Research methods: We used a mixed methods approach to analyse qualitative and quantitative data on the CSR strategies of 66 professional rugby union (Top 14, Aviva Premiership Rugby) and football (Ligue 1, Premier League) clubs over the 2017-2018 season. Results and findings: We found major differences in CSR communication between France and the UK. Communication by French clubs tends to highlight sport’s values, involve few media channels, whereas communication by UK clubs explicitly vaunts their social responsibility and involves numerous channels. In the case of CSR implementation, there are similarities between French and UK clubs, especially in the fields their CSR initiatives cover (e.g., health, diversity), as well as differences. However, the scope of initiatives varies more between sports than between countries, with football demonstrating a more international outlook than rugby. Implications: This article expands Matten and Moon’s (2008) implicit-explicit CSR framework by identifying the influence of interactions between sectorial/field-level factors and national/macro-level factors on CSR practices, and by distinguishing between CSR communication and CSR implementation. Our results throw light on the shift from implicit to explicit CSR in French professional sport

DeepCity: A Feature Learning Framework for Mining Location Check-ins

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly

Social exclusion and economic growth at the European Union: can social marketing and behavioral economics help us to overcome the problem?

The problem of poverty and social exclusion is growing nowadays in the European Union context, according to Eurostat (2015 and 2016). And, in spite of the fact that Europe 2020 Strategy is apparently focused on that situation, the perspectives are not promising. What could be happening? In this paper we analyze this issue from a Macromarketing approach, including elements from Behavioral Economics (stigmatization process and stress coping theories, going further than the “homo economicus” traditional model) to reach a better understanding, and recommending a combined public-private response to overcome the problem, using the elements that Marketing provide us (such as Social Marketing, Macro-social Marketing, Corporate Social Marketing and also traditional Commercial Marketing techniques, under a “fortune at the bottom of the pyramid” approach). Doing so, we do not only want to eradicate this sort of curse, but also to boost economic growth in an effective inclusive manner.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Barriers and drivers to energy efficiency? A New taxonomical approach

This paper develops a new systematic classification and explanation of barriers and drivers to energy efficiency. Using an `actor oriented approach', the paper tries to identify (i) the drivers and barriers that affect the success or failure of energy efficiency investments and (ii) the institutions that are responsible for the emergence of these barriers and drivers. This taxonomy aims to synthesise ideas from three broad perspectives, viz., micro (project/end user), meso (organization), and macro (state, market, civil society). The paper develops a systematic framework by looking at the issues from the perspective of different actors. This not only aids the understanding of barriers and drivers; it also provides scope for appropriate policy interventions. This focus will help policy-makers evaluate to what extent future interventions may be warranted and how one can judge the success of particular interventions.