Emotions In Social Media Research Articles

Impact of Gestalt and Reality Counseling on Reducing Junior High School Students’ Anger in Social Media

Adolescent emotional development can be enhanced by effectively managing conflicts in everyday life, such as those encountered on social media. This study aims to evaluate the effectiveness of Gestalt group counseling with Rehearsal techniques and Reality group counseling with Expressive Writing techniques in mitigating anger emotions in students' social media interactions. Conducted as an experimental research with a pre-test-post-test two-treatment design, the study utilized purposive sampling to select 16 eighth-grade students from State Junior High School 2 Wagir, Malang Regency, Indonesia who demonstrated high levels of anger in social media. The participants were divided into two groups: one receiving Gestalt group counseling with Rehearsal techniques and the other Reality group counseling with Expressive Writing techniques. Data on anger emotions were collected using a specific anger instrument and analyzed with a t-Test. The findings indicate that: (1) Gestalt group counseling with Rehearsal techniques is effective in reducing anger emotions in students on social media; (2) Reality group counseling with Expressive Writing techniques is effective in reducing anger emotions in students on social media; (3) Gestalt group counseling with Rehearsal techniques is more effective than Reality group counseling with Expressive Writing techniques in reducing anger emotions in students on social media. The outcomes of this research may provide school counselors and researchers with additional references for the application of Gestalt group counseling in reducing anger emotions in social media among students.

Social Media Emotions and IPO Returns

AbstractI explore how investor emotions on StockTwits and Twitter affect initial public offering (IPO) returns. High pre‐IPO enthusiasm is linked to greater initial returns but eventual long‐term underperformance. IPOs with strong early excitement average a 29.73% initial return, but suffer a −8.22% long‐term industry‐adjusted return, showing a gap between early optimism and later results. Analysis of investor communication reveals that financial language and existing information influence these outcomes. There is a growing caution among frequent IPO investors, likely due to past experiences. Despite this, firms with initial high optimism continue to attract postlaunch interest, contradicting their long‐term underperformance.

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations.

Viral Fear or Panic Myth? Emotions in Ebola News and Social Media Responses

The COVID‐19 pandemic has catalyzed debates about how the public and leaders respond to health threats and the role that the media and emotions play in these responses. Predating COVID‐19, the 2014 Ebola outbreak can serve as a case to examine the constructions and pervasiveness of fear discourse and other emotions in news and social media. In this mixed‐method study, we examine fear discourse in web‐based and traditional newspaper headlines and emergent emotions in social media data (Twitter) during the peak of Ebola coverage. Users discuss fear on Twitter in a variety of ways and there was an increase in Tweets following the first Ebola case in the United States. However, it is humor, not fear, that is the most dominant theme in Twitter responses. Claims by health leaders and media scholars, that information technology and social media spread fear, receive limited support. Prevalence of different emotions vary across format (headlines and social media) and have important implications for understanding the myths and realities of public responses to health threats.

The impact of social comparison on emotions in social media: The regulation of negative emotions by cognitive reappraisal strategies

Research on the emotional issues caused by upward social comparison, combined with previous research, proposes a cognitive reappraisal strategy in emotion regulation strategies to address this negative emotion, and uses interviews and questionnaire surveys to examine the effectiveness of this strategy. The results show that cognitive reappraisal strategies have a significant improvement effect on the negative emotions caused by upward social comparison. During the research process, it was also found that educational status has an impact on the understanding and use of cognitive reappraisal strategies, and higher educated individuals are more able to fully utilize cognitive reappraisal strategies.

Emotional straying: Flux and management of women’s emotions in social media

In recent years, social media, which has emerged with the core focus on interaction within "acquaintance networks," has gradually been infiltrated by "strangers," leading to the disorientation of many users, especially women, amidst the diverse and intricate social platforms and emotional landscapes. Grounded in the experiential perspective of social media users, this study explores the correlations among woman emotions, satisfaction, and behavior, starting from the standpoint of the impact of social media. Through in-depth interviews with woman cohorts in China, various dimensions such as emotional fluctuations, satisfaction levels, and behaviors in social media were examined. The findings reveal that emotional expression serves as a primary motivation and purpose for users to sustain their engagement with social media. Additionally, emotional masking represents a proactive operational behavior induced by the needs for social relationship maintenance and the accumulation of social capital. Furthermore, emotional management manifests as user-initiated abandonment or shift of social media activities in response to perceived emotional stress. On this basis, a conceptual model integrating woman emotions, satisfaction, and behavior in the context of social media was constructed. The outcomes of this research hold significant theoretical and practical implications for future studies on woman emotions and behaviors, as well as for the development of social media functionalities, content management, public media usage, and psychological health interventions.

Gender-Based Violence Narratives in Internet-Based Conversations in Nigeria: Social Listening Study.

Overcoming gender inequities is a global priority recognized as essential for improved health and human development. Gender-based violence (GBV) is an extreme manifestation of gender inequities enacted in real-world and internet-based environments. In Nigeria, GBV has come to the forefront of attention since 2020, when a state of emergency was declared due to increased reporting of sexual violence. Understanding GBV-related social narratives is important to design public health interventions. We explore how gender-related internet-based conversations in Nigeria specifically related to sexual consent (actively agreeing to sexual behavior), lack of consent, and slut-shaming (stigmatization in the form of insults based on actual or perceived sexuality and behaviors) manifest themselves and whether they changed between 2017 and 2022. Additionally, we explore what role events or social movements have in shaping gender-related narratives in Nigeria. Social listening was carried out on 12,031 social media posts (Twitter, Facebook, forums, and blogs) and almost 2 million public searches (Google and Yahoo search engines) between April 2017 and May 2022. The data were analyzed using natural language processing to determine the most salient conversation thematic clusters, qualitatively analyze time trends in discourse, and compare data against selected key events. Between 2017 and 2022, internet-based conversation about sexual consent increased 72,633%, from an average 3 to 2182 posts per month, while slut-shaming conversation (perpetrating or condemning) shrunk by 9%, from an average 3560 to 3253 posts per month. Thematic analysis shows conversation revolves around the objectification of women, poor comprehension of elements of sexual consent, and advocacy for public education about sexual consent. Additionally, posters created space for sexual empowerment and expressions of sex positivity, pushing back against others who weaponize posts in support of slut-shaming narrative. Time trend analysis shows a greater sense of empowerment in advocating for education around the legal age of consent for sexual activity, calling out double standards, and rejecting slut-shaming. However, analysis of emotions in social media posts shows anger was most prominent in sexual consent (n=1213, 73%) and slut-shaming (n=226, 64%) posts. Organic social movements and key events (#ArewaMeToo and #ChurchToo, the #SexforGrades scandal, and the #BBNaija television program) played a notable role in sparking discourse related to sexual consent and slut-shaming. Social media narratives are significantly impacted by popular culture events, mass media programs, social movements, and micro influencers speaking out against GBV. Hashtags, media clips, and other content can be leveraged effectively to spread awareness and spark conversation around evolving gender norms. Public health practitioners and other stakeholders including policymakers, researchers, and social advocates should be prepared to capitalize on social media events and discourse to help shape the conversation in support of a normative environment that rejects GBV in all its forms.

Social media emotions annotation guide (SMEmo): Development and initial validity.

The proper measurement of emotion is vital to understanding the relationship between emotional expression in social media and other factors, such as online information sharing. This work develops a standardized annotation scheme for quantifying emotions in social media using recent emotion theory and research. Human annotators assessed both social media posts and their own reactions to the posts' content on scales of 0 to 100 for each of 20 (Study 1) and 23 (Study 2) emotions. For Study 1, we analyzed English-language posts from Twitter (N = 244) and YouTube (N = 50). Associations between emotion ratings and text-based measures (LIWC, VADER, EmoLex, NRC-EIL, Emotionality) demonstrated convergent and discriminant validity. In Study 2, we tested an expanded version of the scheme in-country, in-language, on Polish (N = 3648) and Lithuanian (N = 1934) multimedia Facebook posts. While the correlations were lower than with English, patterns of convergent and discriminant validity with EmoLex and NRC-EIL still held.Coder reliability was strong across samples, with intraclass correlations of .80 or higher for 10 different emotions in Study 1 and 16 different emotions in Study 2.This research improves the measurement of emotions in social media to include more dimensions, multimedia, and context compared to prior schemes.

Is Digital Social Communication Effective for Social Relationship? A Study of Online Brand Communities

The rise of the Internet has enabled the consumers to share their brand related emotion in social media brand communities. Members of brand communities frequently use the company’s intellectual and cultural assets, which further foster the forming of close bonds. It improves community members’ social ties and reciprocity standards. As a result, the brand community members become more cooperative. The stimulus-organism-response paradigm has been followed as theoretical framework to determine consumers’ social participation as stimuli which affect their social capital—bridging and bonding (organism) that ultimately improves their brand commitment (response). Select Facebook brand communities provided the data for this investigation. Also, structural equation modeling, which uses different fit indices to test the theoretical framework, has been done. Innovative aspect of this study is how online social interactions between consumer brand communities influence consumers’ social capital, which in turn influences consumers’ brand attitude. Additionally, by creating brand communities for their brands, brand managers can capitalize on the satisfactions of the members. Based on the uses and gratification theory, the study that highlights members’ desire to participate in brand communities’ events can be used by brand managers to advance their businesses.

A Comparative Analysis of Decision Tree and Support Vector Machine on Suicide Ideation Detection

Suicide and self-harm are the most severe public health issues, especially in this modern era. On other side, the improvement of technology helps people can be easier to share their emotion in social media. This led us to think of making a Suicide Ideation Detection to prevent any potential suicide action being committed. Our aim for this research is to increase the capabilities of Decision Tree and Support Vector Machine algorithm, enhancing the performance of already existing research about suicide ideation detection with improved accuracy. By comparing DT and SVM, we hope we can develop a model that can effectively detect suicidal behavior, leading to a better prevention strategy, and minimizing the number of suicide attempts. The dataset used is from subreddit forums “SuicideWatch”, “depression”, and “teenagers”. We discovered that SVM performs better than Decision Tree with an overall score of 91,89% whereas Decision Tree with an overall score of 81,47%.

Social Media Emotions and IPO Returns

Social Media Emotions and IPO Returns

Untangling the relationship between work pressure and emotions in social media: a quantitative empirical study of construction industry

PurposeThe booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.Design/methodology/approachThis study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.FindingsThe research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.Practical implicationsThe positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.Originality/valueThe research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

Sonification of Emotion in Social Media: Affect and Accessibility in Facebook Reactions

Facebook Reactions are a collection of animated icons that enable users to share and express their emotions when interacting with Facebook content. The current design of Facebook Reactions utilizes visual stimuli (animated graphics and text) to convey affective information, which presents usability and accessibility barriers for visually-impaired Facebook users. In this paper, we investigate the use of sonification as a universally-accessible modality to aid in the conveyance of affect for blind and sighted social media users. We discuss the design and evaluation of 48 sonifications, leveraging Facebook Reactions as a conceptual framework. We conducted an online sound-matching study with 75 participants (11 blind, 64 sighted) to evaluate the performance of these sonifications. We found that sonification is an effective tool for conveying emotion for blind and sighted participants, and we highlight sonification design strategies that contribute to improved efficacy. Finally, we contextualize these findings and discuss the implications of this research with respect to HCI and the accessibility of online communities and platforms.

A multi-label emoji classification method using balanced pointwise mutual information-based feature selection

The availability of social media such as twitter allows users to express their feeling, emotions and opinions toward a topic. Emojis are graphic symbols that are regarded as the new generation of emoticons and an effective way of conveying feelings and emotions in social media. With the surging popularity of Emojis, the researchers in the area of Emotion Classification strive to understand the emotion correlated to each Emoji. Two of the most the successful approaches in emoji analysis rely on: 1) official Unicode description and 2) manually built emoji lexicons. Since the use of emoji is socially determined, the former approach is not aligned with intended semantic and usage, which leads researchers to opt for emoji lexicons. To overcome problem of lexicon-based approach, we proposed a method to classify emojis automatically. Therefore, we present a modified Pointwise Mutual Information (PMI) method, called Balanced Pointwise Mutual Information-Based (B-PMI), to develop a balanced weighted emoji classification based on the semantic similarity. Further, deep neural network is used to represent emoji in vector form (emoji embedding) to extend the pre-trained word embeddings. We carefully evaluated the proposed method in multiple twitter datasets that are employed in sentiment and emotion classification using machine learning (ML) and deep learning (DL) approaches. In both approaches, extending word embedding with the proposed emoji embedding improved results. The DL-based approach achieved the highest f1-score of 70.01% for sentiment classification, and accuracy score of 56.36% for emotion classification. ML-based approach obtained accuracy score of 52.17% in emotion classification.

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India During COVID-19 Infodemic

COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people’s health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID-19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger’s causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.

Actors, Partisan Inclination, and Emotions: An Analysis of Government Shutdown News Stories Shared on Twitter

Scholars have recognized emotion as an increasingly important element in the reception and retransmission of online information. In the United States, because of existing differences in ideology, among both audiences and producers of news stories, political issues are prone to spark considerable emotional responses online. While much research has explored emotional responses during election campaigns, this study focuses on the role of online emotion in social media posts related to day-to-day governance in between election periods. Specifically, this study takes the 2018–2019 government shutdown as its subject of investigation. The data set shows the prominence of journalistic and political figures in leading the discussion of news stories, the nuance of emotions employed in the news frames, and the choice of pro-attitudinal news sharing.

ГЕНДЕРНАЯ СПЕЦИФИКА ВЫРАЖЕНИЯ ЭМОЦИЙ В ДИСКУРСЕ СОЦИАЛЬНЫХ СЕТЕЙ

The article deals with the gender specificity of the expression of emotions in social media. A person perceives and reflects the world around him / her and fixes these ideas in language. It is noted that the emotional assessment of reality is displayed both in the semantics of words, phrases, various syntactic structures, and non-verbal communication as well. The gender factor is considered to be one of the essential characteristics of the personality and affects the identification of the speaking subject by other members of the society.

ГЕНДЕРНАЯ СПЕЦИФИКА ВЫРАЖЕНИЯ ЭМОЦИЙ В ДИСКУРСЕ СОЦИАЛЬНЫХ СЕТЕЙ

The article deals with the gender specificity of the expression of emotions in social media. A person perceives and reflects the world around him / her and fixes these ideas in language. It is noted that the emotional assessment of reality is displayed both in the semantics of words, phrases, various syntactic structures, and non-verbal communication as well. The gender factor is considered to be one of the essential characteristics of the personality and affects the identification of the speaking subject by other members of the society.

In Cold Weather We Bark, But in Hot Weather We Bite: Patterns in Social Media Anger, Aggressive Behavior, and Temperature

Hotter weather is associated with aggressive crime. However, it is not well known if similar relationships apply to online aggression. This study uses anger counts derived from Twitter posts (tweets) and assault counts in New South Wales, Australia, to investigate if they share a similar relationship with temperature, and to determine if online anger is a predictor of assault. Results indicated that the relationships were largely inverse—assault counts were higher in summer than winter, while angry tweet counts were lower. As daily maximum temperatures rose, assault counts increased while angry tweet counts decreased. Angry tweet counts were inversely associated with assaults, with an increase in tweets signaling decreasing assaults. There are several plausible explanations for the dissimilarities including the impact of temperature on behavior, socio-demographic differences, and data collection methods. The findings of this study add to the growing literature in social media emotion and its relationship with temperature.