Digital neighborhoods

With the advent of ‘big data’ there is an increased interest in using social media to describe city dynamics. This paper employs geo-located social media data to identify ‘digital neighborhoods’ – those areas in the city where social media is used more often. Starting with geo-located Twitter and Foursquare data for the New York City region in 2014, we applied spatial clustering techniques to detect significant groupings or ‘neighborhoods’ where social media use is high or low. The results show that beyond the business districts, digital neighborhoods occur in communities undergoing shifting socio-demographics. Neighborhoods that are not digitally oriented tend to have higher proportion of minorities and lower incomes, highlighting a social–economic divide in how social media is used in the city. Understanding the differences in these neighborhoods can help city planners interested in generating economic development proposals, civic engagement strategies, and urban design ideas that target these areas

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Political influencers/leaders on Twitter. An analysis of the Spanish digital and media agendas in the context of the Catalan elections of 21 December 2017

A new politics, linked to the influencer/leader and to the empowerment of the public on social networking sites, is currently marking the media agenda. In light of this, the aim of this study is to gain further insights into the polarization and influence of political messages on Twitter and levels of user participation, in a context marked by social movements and the counter-power of citizenship. Based on a triangulated methodology of quantitative and qualitative-discursive content analysis, all the tweets were quantified (3,562), selecting only those pertaining to the elections (526) posted by the pro-independence and constitutionalist candidates of the parties obtaining the highest number of votes, plus 144,382 user engagement metrics and 68 front pages of the mainstream Spanish and Catalan press. The results point to a unidirectional use of Twitter by political leaders, a higher user response rate, and the influence of the digital political agenda on its media counterpart

Social Networks, Political Discourse and Polarization during the 2017 Catalan elections

This thesis investigates the political process in Spain and Catalonia during the Catalan election in December 2017. This regional election was unusual because of the independence process in Catalonia and its repression. Two parties, Ciudadanos (anti-independence) and Podemos (ambiguous position) and their leaders’ activity in Twitter was analyzed. It was explored from three perspectives: social networks, lexical and emotional discourse and ideological polarization. Firstly, social networks were used to see the properties of the support communities of both parties. Interestingly unlike Ps, Ciudadanos’ (Cs) metrics of cohesion showed that political communities of this party in Spain and Catalonia were remarkably well integrated. Secondly, using machine learning techniques, discourse cohesiveness of Ps and Cs’ politicians was analyzed regarding the lexical and emotional content of their messages. The results showed that even though Cs’ politicians were more lexically similar, Ps’ were more similar in terms of emotions. Specifically, the study of emotions in the discourse shed light on populist messages from Cs. This party used anger and disgust to take advantage the polarized political scenario. Lastly, with a sample of users (N=2000) in Twitter, the relationship between dispositional emotions and ideological polarization was investigated. Results showed that users predisposed to anger were significantly more polarized and those predisposed to fear were significantly less polarized. Interestingly, even though predisposition to fear decreased polarization, the interaction between fear and anger significantly increased it. These results have interesting implications regarding the increasing opportunities of politicians to target the electorate based on personal characteristics

Evolution and structuration of opinion communities in social conflicts

Traditionally, political polarization has been an important topic of analysis on the social and political sciences, but in recent years, due to the paradigm shift of political participation and the digital record that it generates associated to the universalization of the Internet and the emergence of online social networks, it can be seen that different scienti c disciplines related to complex networks have focused on the study of political polarization, elections predictions or protests. In this context, this work consists of an extense analysis of the society behavior on a social network when there is a tense situation, using as case of study an ongoing conflict in Spain, the catalan independence. This conflict, besides the political tension that generates, provides us a unique ground truth for user classi cation. Taking advantage of this fact, we perform two strategies to classify ideologically opposite users and several analyses to detect political communities, study the political polarization underlying this great amount of data and evaluate temporal dynamics

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following