Hot weather, hot topic:Polarization and sceptical framing in the climate debate on Twitter

Extreme weather events like the heat wave of 2018 reinforce public attention for climate change. Social media platforms facilitate, define and amplify debate about this topic. They give rise to counterpublic spaces through which counterpublics such as climate sceptics get a stage they would not easily get in mainstream media. Previous research suggests that sceptics use these spaces as safe havens, but also as bases for interventions in the hegemonic debate. Applying a multideterminant frame model, we analyse the Twitter debate among climate change ‘sceptics’ and ‘believers’. We study all tweets in which the heat wave was related to climate change and which were shared by Dutch and Flemish users between 28 July 2018 and 4 August 2018. Laying bare the worldviews underlying the frames of sceptics and non-sceptics, we first demonstrate the diversity of – unilaterally interacting – ideological interests. Building upon this analysis of the scope of the debate and analysing its form, we show that both groups mostly use similar antagonistic strategies to delegitimize and denaturalize their out-groups. We argue that these interventions promote polarization rather than a constructive agonistic debate. As such, this study refutes previous studies that consider sceptic frames as deconstructive and non-sceptic frames as constructive

Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGThis paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.Xunta de Galicia | Ref. ED431C2018/55-GRCMinisterio de Ciencia e Innovación | Ref. PID2020–113673RB-I00Xunta de Galicia y European Regional Development Fund | Ref. ED431G2019/06Fundação para a Ciência e a Tecnologia | Ref. UIDB/04469/202

Exploring Sentiment Analysis on Twitter: Investigating Public Opinion on Migration in Brazil from 2015 to 2020

openTechnology has reshaped societal interaction and the expression of opinions. Migration is a prominent trend, and analysing social media discussions provides insights into societal perspectives. This thesis explores how events between 2015 and 2020 impacted Brazilian sentiment on Twitter about migrants and refugees. Its aim was to uncover the influence of key sociopolitical events on public sentiment, clarifying how these echoed in the digital realm. Four key objectives guided this research: (a) understanding public opinions on migrants and refugees, (b) investigating how events influenced Twitter sentiment, (c) identifying terms used in migration-related tweets, and (d) tracking sentiment shifts, especially concerning changes in government. Sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) was employed to analyse tweet data. The use of computational methods in social sciences is gaining traction, yet no analysis has been conducted before to understand the sentiments of the Brazilian population regarding migration. The analysis underscored Twitter's role in reflecting and shaping public discourse, offering insights into how major events influenced discussions on migration. In conclusion, this study illuminated the landscape of Brazilian sentiment on migration, emphasizing the significance of innovative social media analysis methodologies for policymaking and societal inclusivity in the digital age

Green energy: identifying development trends in society using Twitter data mining to make strategic decisions

This study analyzes Twitter’s contribution to green energy. More than 200,000 global tweets sent during 2020 containing the terms “green energy” OR “greenenergy” were analyzed. The tweets were captured by web scraping and processed using algorithms and techniques for the analysis of massive datasets from social networks. In particular, relationships between users (through mentions) were determined according to the Louvain multilevel algorithm to identify communities and analyze global (density and centralization) and node-level (centrality) metrics. Subsequently, the content of the conversation was subject to semantic analysis (co-occurrence of the most relevant words), hashtag analysis (frequency analysis), and sentiment analysis (using the Vader model). The results reveal nine main communities and their leaders, as well as three main topics of conversation and the emotional state of the digital discussion. The main communities revolve around politics, socioeconomic issues, and environmental activism, while the conversations, which have developed mostly in positive terms, focus on green energy sources and storage, being aligned with the main communities identified, i.e., on political, socioeconomic, and climate change issues. Although most of the conversations have been about socioeconomic issues, the presence of leading company accounts was minor. The main aim of this work is to take the first steps toward an innovative competitive intelligence methodology to study and determine trends within different scientific fields or technologies in society that will enable strategic decisions to be made

“You’re trolling because…” – A Corpus-based Study of Perceived Trolling and Motive Attribution in the Comment Threads of Three British Political Blogs

This paper investigates the linguistically marked motives that participants attribute to those they call trolls in 991 comment threads of three British political blogs. The study is concerned with how these motives affect the discursive construction of trolling and trolls. Another goal of the paper is to examine whether the mainly emotional motives ascribed to trolls in the academic literature correspond with those that the participants attribute to the alleged trolls in the analysed threads. The paper identifies five broad motives ascribed to trolls: emotional/mental health-related/social reasons, financial gain, political beliefs, being employed by a political body, and unspecified political affiliation. It also points out that depending on these motives, trolling and trolls are constructed in various ways. Finally, the study argues that participants attribute motives to trolls not only to explain their behaviour but also to insult them

Social Media And Credibility Indicator: The Effects Of Bandwagon And Identity Cues Within Online Health And Risk Contexts

Three studies were conducted to investigate how social media affordances influence individuals’ source credibility perceptions in risk situations. The MAIN model (Sundar, 2008), warranting theory (Walther & Parks, 2002), and signaling theory (Donath, 1999) served as the theoretical framework to examine the effects of bandwagon cues and identity cues embedded in retweets and users’ profile pages for health and risk online information processing. Study One examines whether bandwagon heuristics triggered by retweets would influence individuals’ source credibility judgments. Study Two investigates how bandwagon heuristics interact with different identity heuristics in credibility heuristics on an individual level. Study Three explores bandwagon heuristics at the organizational level. Three post-test only experiments with self-report online surveys were conducted to investigate the hypothesis and research questions. Results indicate that different online heuristic cues impact the judgments of competence, goodwill, and trustworthiness at different levels. Authority strongly influenced source credibility perceptions. A reverse-bandwagon effect was observed in influencing source credibility judgments. Theoretical and practical implications are discussed

Negative emotions set in motion : the continued relevance of #GamerGate

This chapter aims at making sense of the #GamerGate (#GG) online harassment campaign that was particularly active in 2014–2015 but to this day continues to produce hateful speech against certain ideologies and minorities in gaming culture. The campaign was especially successful at building online visibility through harassment, and the affective resonances of the issues it raised have since translated into general online campaigning how-to’s, financial earnings, and even political action outside of the gaming sphere. Although the primary breeding ground for this movement was 4chan (and later, 8chan), it only reached public awareness and visibility – hence, effectiveness – through Twitter and, to a lesser extent, through YouTube. In order to understand the emotional charge and political relevance of this campaign, we rely on both quantitative and qualitative activity analyses of the Twitter users that use the hashtag #GamerGate between 2014 and 2019. In addition to analyzing who were the most active tweeters and what kind of resonance their tweets elicited, we looked into the emotional qualities of their communication. The communication strategies of #GG tweeters took advantage of the language and cultural references of the target demographic to drive a set of topics into public discourse and, further, to political activism. This discourse utilized a combination of affective modes, based mainly on resentment and schadenfreude, that we see echoing in many places on the internet. In the end, we argue that while #GG may have been only one instance of a campaign with harassment elements, the sentiments it cultivated and amplified as well as its operational logics have since been successfully employed in many similar online movements, including the current political campaigning associated with the so-called alt-right.fi=vertaisarvioitu|en=peerReviewed