7 research outputs found

    Emotion Dynamics of Public Opinions on Twitter

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    [EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). 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    Flipping Stance: Social Influence on Bot's and Non Bot's COVID Vaccine Stance

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    Social influence characterizes the change of opinions in a complex social environment, incorporating an individual's past stances and the impact of interpersonal influence through the social network influence. In this work, we observe stance changes towards the coronavirus vaccine on Twitter from April 2020 to May 2021, where 1\% of the agents exhibit the stance flipping behavior, of which 53.7\% are identified bots. We then propose a novel social influence model to characterize the change in stance of agents. This model considers an agent's and his neighbor's past tweets and the overall network structure towards a stance score. In our experiments, the model achieves 86\% accuracy. In our analysis, bot agents require lesser social influence to flip stances and a larger proportion of bots flip.Comment: To appear in The Second International MIS2 Workshop: Misinformation and Misbehavior Mining on the We

    Social Media Analytics in Disaster Response: A Comprehensive Review

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    Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

    Sampling Twitter users for social science research: Evidence from a systematic review of the literature

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    All social media platforms can be used to conduct social science research, but Twitter is the most popular as it provides its data via several Application Programming Interfaces, which allows qualitative and quantitative research to be conducted with its members. As Twitter is a huge universe, both in number of users and amount of data, sampling is generally required when using it for research purposes. Researchers only recently began to question whether tweet-level sampling—in which the tweet is the sampling unit—should be replaced by user-level sampling—in which the user is the sampling unit. The major rationale for this shift is that tweet-level sampling does not consider the fact that some core discussants on Twitter are much more active tweeters than other less active users, thus causing a sample biased towards the more active users. The knowledge on how to select representative samples of users in the Twitterverse is still insufficient despite its relevance for reliable and valid research outcomes. This paper contributes to this topic by presenting a systematic quantitative literature review of sampling plans designed and executed in the context of social science research in Twitter, including: (1) the definition of the target populations, (2) the sampling frames used to support sample selection, (3) the sampling methods used to obtain samples of Twitter users, (4) how data is collected from Twitter users, (5) the size of the samples, and (6) how research validity is addressed. This review can be a methodological guide for professionals and academics who want to conduct social science research involving Twitter users and the Twitterverse.info:eu-repo/semantics/publishedVersio

    Analysing the discursive psychology used within digital media to influence public opinions concerning female child-killers

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    Discursive psychology is used to invoke emotion and social action within receivers, and widespread media is notorious for utilizing these linguistic features to negatively skew the public opinion of an individual or group. This study aims to investigate through discursive thematic analysis the ways in which digitised media articles utilise linguistic features and discursive devices to invoke emotion within readers, and in turn influence their opinions concerning female child-killers. The data gathered for this piece of research were 9 digital newspaper articles published between 2017 and 2021 by any of the top 10 most-read titles according to YouGov (2021) and were sourced using Google Chrome. The key terms used to locate these articles were the names “Rachel Henry”, “Tracey Connelly” and “Louise Porton” followed by the names of the top 10 most-read titles (e.g., “Rachel Henry Daily Mail”). The themes identified suggest a consistent aim within the media to negatively influence the public opinion of the offenders in question by using discursive devices and psychological categories to attack and invalidate these offenders and portray them as being evil, inhuman, delusional individuals who are inherently different from “normal” members of society. The findings produced within this research may have implications regarding the future of mainstream media reporting, as they suggest an excessive use of strategically influential linguistic features within digital newspapers to create extreme negative representations of women who offend, which may prove detrimental to their future access to, and experience of reformation and rehabilitation.N/

    Environmental Disasters and Individuals’ Emergency Preparedness:

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    Environmental disasters are becoming more frequent. These disasters not only include the most common natural disasters, but also include man-made disasters, such as public health, accident disasters, etc., which have caused greater damage to human society and cities. Because of the limitations of a single government-led model in emergency response, the emergency preparedness of communities, families and individuals are more important. In particular, the emergency preparedness psychology and behavior of individuals directly determine whether or not they can effectively protect themselves and their families in the first time of disaster. This Special Issue focuses on environmental disasters and individuals’ emergency preparedness in the perspective of psychology and behavior
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