Stance and influence of Twitter users regarding the Brexit referendum

Veganuary and the vegan sausage (t)rolls: conflict and commercial engagement in online climate-diet discourse

Social media platforms have become critical venues for a wide spectrum of influence campaigns, from activism to advertising. Sometimes these two ends overlap and it remains unknown how the latter might impact the former. Situated within contemporary scholarship on vegan activism, this work examines corporate involvement with the Veganuary 2019 campaign on Twitter, as well as the antagonistic backlash it received. We find that the activists and commercial entities engage mostly separate audiences, suggesting that commercial campaigns do little to drive interactions with Veganuary activism. We also discover strong threads of antagonism reflecting the "culture wars" surrounding discussions of veganism and climate-diet science. These findings inform our understanding of the challenges facing climate-diet discourses on social media and motivate further research into the role of commercial agents in online activism.

Research using hashtags: A meta-synthesis

In the last 20 years, research using hashtags has grown considerably. The changes that occurred in the digital environment have influenced their diffusion and development. Today, there is considerable research on hashtags, their use, and on hashtag activism. Likewise, there is a growing interest in their descriptive measures and their metrics. This article aimed to provide a review of this area of research and studies to outline the traits of hashtag research, which are yet nascent. To achieve this, we used a meta-study to produce a meta-synthesis capable of bringing out similarities and differences in research using hashtags and identifying spaces for the generation of new knowledge.

Developing insights from the collective voice of target users in Twitter

This study develops a pragmatic scheme that facilitates insight development from the collective voice of target users in Twitter, which has not been considered in the existing literature. While relying on a wide range of existing approaches to Twitter user profiling, this study provides a novel and generic procedure that enables researchers to identify the right users in Twitter and discover topical and social insights from their tweets. To identify a target audience of Twitter users that meets certain criteria, we first explore user profiling, potentially followed by text-based, customized user profiling leveraging hashtags as features for machine learning. We then present how to mine popular topics and influential actors from Twitter data. Two case studies on 16 thousand young women interested in fashion and 68 thousand people sharing the same interest in the Me Too movement indicate that our approach facilitates discovery of social trends among people in a particular domain.

A Dissemination Model Based on Psychological Theories in Complex Social Networks

Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this article, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of the 2019 novel Corona Virus Disease (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

Retweet communities reveal the main sources of hate speech

We address a challenging problem of identifying main sources of hate speech on Twitter. On one hand, we carefully annotate a large set of tweets for hate speech, and deploy advanced deep learning to produce high quality hate speech classification models. On the other hand, we create retweet networks, detect communities and monitor their evolution through time. This combined approach is applied to three years of Slovenian Twitter data. We report a number of interesting results. Hate speech is dominated by offensive tweets, related to political and ideological issues. The share of unacceptable tweets is moderately increasing with time, from the initial 20% to 30% by the end of 2020. Unacceptable tweets are retweeted significantly more often than acceptable tweets. About 60% of unacceptable tweets are produced by a single right-wing community of only moderate size. Institutional Twitter accounts and media accounts post significantly less unacceptable tweets than individual accounts. In fact, the main sources of unacceptable tweets are anonymous accounts, and accounts that were suspended or closed during the years 2018–2020.

The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study

Background

In November 2018, a Chinese researcher reported that his team had applied clustered regularly interspaced palindromic repeats or associated protein 9 to delete the gene C-C chemokine receptor type 5 from embryos and claimed that the 2 newborns would have lifetime immunity from HIV infection, an event referred to as #GeneEditedBabies on social media platforms. Although this event stirred a worldwide debate on ethical and legal issues regarding clinical trials with embryonic gene sequences, the focus has mainly been on academics and professionals. However, how the public, especially stratified by geographic region and culture, reacted to these issues is not yet well-understood.

Objective

The aim of this study is to examine web-based posts about the #GeneEditedBabies event and characterize and compare the public’s stance across social media platforms with different user bases.

Methods

We used a set of relevant keywords to search for web-based posts in 4 worldwide or regional mainstream social media platforms: Sina Weibo (China), Twitter, Reddit, and YouTube. We applied structural topic modeling to analyze the main discussed topics and their temporal trends. On the basis of the topics we found, we designed an annotation codebook to label 2000 randomly sampled posts from each platform on whether a supporting, opposing, or neutral stance toward this event was expressed and what the major considerations of those posts were if a stance was described. The annotated data were used to compare stances and the language used across the 4 web-based platforms.

Results

We collected >220,000 posts published by approximately 130,000 users regarding the #GeneEditedBabies event. Our results indicated that users discussed a wide range of topics, some of which had clear temporal trends. Our results further showed that although almost all experts opposed this event, many web-based posts supported this event. In particular, Twitter exhibited the largest number of posts in opposition (701/816, 85.9%), followed by Sina Weibo (968/1140, 84.91%), Reddit (550/898, 61.2%), and YouTube (567/1078, 52.6%). The primary opposing reason was rooted in ethical concerns, whereas the primary supporting reason was based on the expectation that such technology could prevent the occurrence of diseases in the future. Posts from these 4 platforms had different language uses and patterns when they expressed stances on the #GeneEditedBabies event.

Conclusions

This research provides evidence that posts on web-based platforms can offer insights into the public’s stance on gene editing techniques. However, these stances vary across web-based platforms and often differ from those raised by academics and policy makers.

Social media analytics system for action inspection on social networks

Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously regarding political topics, often driven by specific events in social life. Real-time analysis of social media can provide valuable feedback and insights to both politicians and news agencies. In this paper, we discuss the design and implementation of a system for tracking and analysing social media. The SocMINT system provides an easy-to-use, visual dashboard to monitor the discussion on specific topics, to capture trends in communities and, by iteratively applying multidimensional data analysis and filtering, to deeply analyse posts and influencers. SocMINT aggregates data from multiple social sources and performs sentiment analysis on textual, visual and mixed content via a specifically designed neural network architecture. The system was applied in a real context by administrative staff of a political party to effectively analyse candidates’ political communication on Facebook, Instagram and Twitter and the related online community reactions and discussion. In the paper, we report on this real-world case study, showing how the system meaningfully captures trends in public opinion, comparing the main KPIs provided by SocMINT with the outcomes of traditional polls.

Characterizing the role of bots’ in polarized stance on social media

There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users’ stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users’ stances towards controversial topics. We analyze users’ online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users’ direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts. Nevertheless, we found that the presence of bots was still connected to users’ stances, especially in an indirect manner, as users are exposed to the content of the bots they follow, rather than by directly interacting with them by retweeting, mentioning, or replying.

Evolution of topics and hate speech in retweet network communities

Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018-2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post.

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

Community evolution in retweet networks

Communities in social networks often reflect close social ties between their members and their evolution through time. We propose an approach that tracks two aspects of community evolution in retweet networks: flow of the members in, out and between the communities, and their influence. We start with high resolution time windows, and then select several timepoints which exhibit large differences between the communities. For community detection, we propose a two-stage approach. In the first stage, we apply an enhanced Louvain algorithm, called Ensemble Louvain, to find stable communities. In the second stage, we form influence links between these communities, and identify linked super-communities. For the detected communities, we compute internal and external influence, and for individual users, the retweet h-index influence. We apply the proposed approach to three years of Twitter data of all Slovenian tweets. The analysis shows that the Slovenian tweetosphere is dominated by politics, that the left-leaning communities are larger, but that the right-leaning communities and users exhibit significantly higher impact. An interesting observation is that retweet networks change relatively gradually, despite such events as the emergence of the Covid-19 pandemic or the change of government.

Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration

The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users-hereby called partisan communities-without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces. As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment.

Moral Expressions in 280 Characters or Less: An Analysis of Politician Tweets Following the 2016 Brexit Referendum Vote

Ideas about morality are deeply entrenched into political opinions. This article examines the online communication of British parliamentarians from May 2017-December 2019, following the 2016 referendum that resulted in Britain's exit (Brexit) from the European Union. It aims to uncover how British parliamentarians use moral foundations to discuss the Brexit withdrawal agreement on Twitter, using Moral Foundations Theory as a classification basis for their tweets. It is found that the majority of Brexit related tweets contain elements of moral reasoning, especially relating to the foundations of Authority and Loyalty. There are common underlying foundations between parties, but parties express opposing viewpoints within a single foundation. The study provides useful insights into Twitter's use as an arena for moral argumentation, as well as uncovers the politician's uses of moral arguments during Brexit agreement negotiations on Twitter. It contributes to the limited body of work focusing on the moral arguments made by politicians through Twitter.

Mainstreaming populism through the Twitter practices of politicians and the news media: A case study of the 2016 Brexit referendum debates

Over recent years, the Western world has witnessed the (re-)rise of populism, which was marginal compared to the (once-)dominant ideologies of globalisation and European integration. This article examines the role played by the Twitter practices of politicians and the news media in mainstreaming populism through a case study of the 2016 Brexit referendum debates. The communicative freedom of politicians and the extensive attention given to them by users enabled the presenting of populist arguments as a legitimate part of debates about the referendum. The news media paid overwhelming attention to politicians and focused on the issues of immigration and the economy in their tweets, creating the sphere of legitimate controversy where populist arguments appeared in parallel with those supporting globalisation and European integration. In this case, the Twitter practices of British politicians and the news media led to the extensive – but largely uncritical – presence and articulation of populist claims on Twitter. Their strong presence alongside pro-EU and pro-globalisation arguments gave these populist perspectives a central place in the debates on the referendum. The mainstreaming of populism through the Twitter practices of politicians and the news media is inextricably linked with, and thus needs to be understood within, the broader context of rising populism where the (once-)dominant ideologies of globalisation and European integration are in decline.

Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of tweets on the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust than polling, our study also suggests that the former can advantageously complement the latter in opinion prediction.

Exploring repost features of police-generated microblogs through topic and sentiment analysis

Purpose

This study aims to explore the repost features of microblogs acting to promote the information diffusion of government-generated content on social media.

Design/methodology/approach

This study proposes a topic−sentiment analysis using a mixed social media analytics framework to analyse the microblogs collected from the Sina Weibo accounts of 30 Chinese provincial police departments. On the basis of this analysis, this study presents the distribution of reposted microblogs and reveals the reposting characteristics of police-generated microblogs (PGMs).

Findings

The experimental results indicate that children’s safety and crime-related PGMs with a positive sentiment can achieve a high level of online information diffusion.

Originality/value

This study is novel, as it reveals the reposting features of PGMs from both a topic and sentiment perspectives, and provides new findings that can inspire users’ reposting behaviour.

Impact investing market on Twitter: influential users and communities

The 2008 financial crisis unveiled the intrinsic failures of the financial system as we know it. As a consequence, impact investing started to receive increasing attention, as evidenced by the high market growth rates. The goal of impact investment is to generate social and environmental impact alongside a financial return. In this paper we identify the main players in the sector and how they interact and communicate with each other. We use Twitter as a proxy of the impact investing market, and analyze relevant tweets posted over a period of ten months. We apply network, contents and sentiment analysis on the acquired dataset. Our study shows that Twitter users exhibit favourable leaning (predominantly neutral or positive) towards impact investing. Retweet communities are decentralised and include users from a variety of sectors. Despite some basic common vocabulary used by all retweet communities identified, the vocabulary and the topics discussed by each community vary largely. We note that an additional effort should be made in raising awareness about the sector, especially by policymakers and media outlets. The role of investors and the academia is also discussed, as well as the emergence of hybrid business models within the sector and its connections to the tech industry. This paper extends our previous study, one of the first analyses of Twitter activities in the impact investing market.