Bow-tie structures of twitter discursive communities

Bow-tie structures were introduced to describe the World Wide Web (WWW): in the direct network in which the nodes are the websites and the edges are the hyperlinks connecting them, the greatest number of nodes takes part to a bow-tie, i.e. a Weakly Connected Component (WCC) composed of 3 main sectors: IN, OUT and SCC. SCC is the main Strongly Connected Component of WCC, i.e. the greatest subgraph in which each node is reachable by any other one. The IN and OUT sectors are the set of nodes not included in SCC that, respectively, can access and are accessible to nodes in SCC. In the WWW, the greatest part of the websites can be found in the SCC, while the search engines belong to IN and the authorities, as Wikipedia, are in OUT. In the analysis of Twitter debate, the recent literature focused on discursive communities, i.e. clusters of accounts interacting among themselves via retweets. In the present work, we studied discursive communities in 8 different thematic Twitter datasets in various languages. Surprisingly, we observed that almost all discursive communities therein display a bow-tie structure during political or societal debates. Instead, they are absent when the argument of the discussion is different as sport events, as in the case of Euro2020 Turkish and Italian datasets. We furthermore analysed the quality of the content created in the various sectors of the different discursive communities, using the domain annotation from the fact-checking website Newsguard: we observe that, when the discursive community is affected by m/disinformation, the content with the lowest quality is the one produced and shared in SCC and, in particular, a strong incidence of low- or non-reputable messages is present in the flow of retweets between the SCC and the OUT sectors. In this sense, in discursive communities affected by m/disinformation, the greatest part of the accounts has access to a great variety of contents, but whose quality is, in general, quite low; such a situation perfectly describes the phenomenon of infodemic, i.e. the access to "an excessive amount of information about a problem, which makes it difficult to identify a solution", according to WHO

Open data governance: civic hacking movement, topics and opinions in digital space

AbstractThe expression 'open data' relates to a system of informative and freely accessible databases that public administrations make generally available online in order to develop an informative network between institutions, enterprises and citizens. On this topic, using the semantic network analysis method, the research aims to investigate the communication structure and the governance of open data in the Twitter conversational environment. In particular, the research questions are: (1) Who are the main actors in the Italian open data infrastructure? (2) What are the main conversation topics online? (3) What are the pros and cons of the development and use (reuse) of open data in Italy? To answer these questions, we went through three research phases: (1) analysing the communication network, we found who are the main influencers; (2) once we found who were the main actors, we analysed the online content in the Twittersphere to detect the semantic areas; (3) then, through an online focus group with the main open data influencers, we explored the characteristics of Italian open data governance. Through the research, it has been shown that: (1) there is an Italian open data governance strategy; (2) the Italian civic hacker community plays an important role as an influencer; but (3) there are weaknesses in governance and in practical reuse

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

Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Using visual analytics to make sense of railway Close Calls

In the big data era, large and complex data sets will exceed scientists’ capacity to make sense of them in the traditional way. New approaches in data analysis, supported by computer science, will be necessary to address the problems that emerge with the rise of big data. The analysis of the Close Call database, which is a text-based database for near-miss reporting on the GB railways, provides a test case. The traditional analysis of Close Calls is time consuming and prone to differences in interpretation. This paper investigates the use of visual analytics techniques, based on network text analysis, to conduct data analysis and extract safety knowledge from 500 randomly selected Close Call records relating to worker slips, trips and falls. The results demonstrate a straightforward, yet effective, way to identify hazardous conditions without having to read each report individually. This opens up new ways to perform data analysis in safety science

Finding polarised communities and tracking information diffusion on Twitter: The Irish Abortion Referendum

The analysis of social networks enables the understanding of social interactions, polarisation of ideas, and the spread of information and therefore plays an important role in society. We use Twitter data - as it is a popular venue for the expression of opinion and dissemination of information - to identify opposing sides of a debate and, importantly, to observe how information spreads between these groups in our current polarised climate. To achieve this, we collected over 688,000 Tweets from the Irish Abortion Referendum of 2018 to build a conversation network from users mentions with sentiment-based homophily. From this network, community detection methods allow us to isolate yes- or no-aligned supporters with high accuracy (90.9%). We supplement this by tracking how information cascades spread via over 31,000 retweet-cascades. We found that very little information spread between polarised communities. This provides a valuable methodology for extracting and studying information diffusion on large networks by isolating ideologically polarised groups and exploring the propagation of information within and between these groups.Comment: 44 pages, 4 appendices, 18 figure

Lexical innovation on the web and social media

This dissertation investigates the emergence and diffusion of English neologisms on the web and social media, employing a data-driven methodology to identify a substantial sample of 851 neologisms. Neologisms are examined from their coining to successful dissemination within the community, with the study revealing a wide spectrum of degrees of diffusion. The exploration extends to studying the usage and diffusion of selected neologisms on the web and on Twitter, with a particular focus on social dynamics and variation among different speaker groups. Moreover, the dissertation probes into semantic innovation, demonstrating substantial socio-semantic variation and polarized public discourse surrounding certain neologisms. The research conducts an extensive analysis of semantic innovation and socio-semantic variation, elucidating significant socio-semantic discrepancies between various communities. The dissertation sheds light on the social and semantic dynamics underpinning the life cycle of neologisms within a linguistically diverse community