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The academic literature reports mixed evidence on how social media platform and message impact consumer engagement. We investigated the effects of three platforms (Facebook, Instagram, and Twitter) and three message themes (sales, informational, and relationship building) on six consumer engagement actions (comment, like, search, share, talk about, and purchase) in a lab experiment. College students responded to social media posts featuring their National Collegiate Athletic Association Division I women's basketball team. Results for platform show that participants were more likely to comment on Facebook and Twitter (vs. Instagram) and more likely to purchase on Twitter (vs. Instagram). Results for message theme show that participants were more likely to comment, like, and share informational and relationship building posts and more likely to purchase after sales posts. Results for message theme vary by gender for search and talk about (with others). These results can help sport marketers develop social media content that drives specific engagement actions. [ABSTRACT FROM AUTHOR]

While the National Collegiate Athletic Association's (NCAA) purpose is to protect college athletes within a hypercommercialized institutional setting, the protections prevent college athletes from accessing the lucrative marketplace. Extant literature has conceptualized the operating functions of the NCAA within the context of a patriarchal framework in which college athletes are infantilized, and authoritative institutional control is thereby justified. However, social media has provided a platform to engage in counter-storytelling and activism. As such, this study examined engagement with college-athlete-led social media activism. Utilizing a content-analysis methodological approach, social media engagement with the Twitter hashtag #NotNCAAProperty was examined over the course of the 2021 NCAA Division I men's basketball tournament. Findings revealed that most interactions were supportive of college athletes and suggest that social media may be a strategic mechanism for college athletes to engage in advocacy initiatives. [ABSTRACT FROM AUTHOR]

The document discusses the importance of embracing Computational Social Science (CSS) approaches to study social media in the context of sport communication. It highlights the challenges faced by scholars in defining social media, the benefits of CSS techniques, and the practical challenges of data collection, cleaning, analysis, and reporting. The document also emphasizes the need for multidisciplinary collaborations, ethical considerations, and the potential limitations of computational research. It concludes by encouraging researchers to leverage traditional methods alongside CSS techniques to advance sport and social media scholarship. [Extracted from the article]

The popularity of social network service users has increased in recent years, altering politicians' interest level in social network services. Given this trend, social network services now play a central role in political communication channels, enabling them to express and share their opinions and news directly with citizens. Therefore, many researchers have attempted to investigate social network service users' political stances and proposed a user's political stance model utilizing this dataset. Understanding and detecting social network services and a user's political stance can play a significant role in marketing strategies and determining election winners. In light of this, the present study examined Twitter from diverse perspectives to analyze and detect a Twitter user's political stance. This study collected Twitter datasets and labeled a user's stance using a clustering approach to determine whether a user was a Democrat or a Republican. After an exploratory analysis of users' tweet content: image and text, user network, and profile description, the tweet user stance detection model was proposed and tested. The results indicated notable differences between Democrats and Republicans from diverse perspectives on Twitter, with an accuracy of 85.35% compared with baseline models. The implications and limitations of this study were discussed based on the results. [ABSTRACT FROM AUTHOR]

On June 27, 2015, the Greek coalition government, led by the left-wing SYRIZA party, announced the July 5th referendum, asking citizens to decide on the adoption of the EU-proposed economic plan. Referendums in Greece are infrequent, and this decision sparked various interpretations of the motives behind it. In events like the 2016 US presidential elections and the Brexit referendum of the same year, public opinion, especially as expressed on platforms like Twitter, often diverged from the narrative set by the news media agenda. The outcome of the Greek referendum reflected Twitter users' preferences more closely, surprising many and challenging the traditional role of news media in shaping public opinion. This paper revisits the 2015 Greek referendum, comparing topics discussed in news media with those on Twitter to understand whether the disparity between the two platforms resulted from the dominance of news media agenda-setting or other factors, such as Twitter's inclination toward alternative voices. The study employs content analysis and topic modeling on a dataset comprising news articles and tweets. Results indicate that the news media agenda predominantly influenced topics discussed by "YES" supporters on Twitter, while it played a less significant role in shaping the topics discussed by "NO" supporters during the 2015 Greek referendum. [ABSTRACT FROM AUTHOR]

Microblog is one of the most popular social media platforms in China. Fine grained sentiment analysis of Chinese microblog comments has attracted much attention. Graph Convolutional Neural Network (GCN) has been broadly used in sentiment analysis but still suffers from emotion misclassification due to the complexity and diversity of Chinese microblogs' syntax structures. To address the issue, we propose a graph pooling method based on self-attention mechanism, namely, AGMPool. The AGMPool pooling method uses graph convolution to calculate its attention score for each graph node and then to filter out nodes with excessive useless information in the graph topology according to these scores, which effectively improves the performance of fine-grained sentiment analysis through GCN. In addition, for better understanding of diverse syntax structures of Chinese microblogs, we propose a microblog fine grained sentiment analysis model, namely, LMG-AGMPool, which combines GCN with the AGMPool pooling method and extracts emotional features based on the syntax structures of text and the importance of words in text. The experimental results indicate that the LMG-AGMPool model has better performance than the traditional methods and the deep learning methods in fine grained sentiment analysis. [ABSTRACT FROM AUTHOR]

Purpose: In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy. Design/methodology/approach: We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the "7.20 Henan Rainstorm" event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels. Findings: Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation. Originality/value: This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192. [ABSTRACT FROM AUTHOR]

Companies worldwide use microblogs to communicate with stakeholders, but there may be cultural differences in how they do it. A content analysis was conducted comparing Twitter (currently known as X) and Weibo accounts of four Western IT companies; Weibo accounts of four similar Chinese companies served as benchmarks. Results show that Western microblog activities differed in many respects from Chinese practices. Specifically, they focused more on technology and less on marketing and community-building. In their localization strategies, Western companies chose to adapt (e.g., paying attention to community building), not adapt (e.g., keeping their technological profile), or cautiously adapt (e.g., using somewhat more emojis or reluctantly experimenting with sweepstakes). Cultural differences on microblogging platforms are comprehensive and multifaceted and cannot be easily reduced to established cultural dimensions. Processes of cultural adaptation, therefore, depend on profound knowledge of the business environment and cultural differences. [ABSTRACT FROM AUTHOR]

In recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurrencies based on different neural networks such as long short‐term memory, convolutional neural network and attention mechanism. Using an ensemble of three pre‐trained language models, we extracted sentiment of cryptocurrency‐related tweets posted between 1 January 2021 and 31 December 2021. We constructed 20 different versions of our model and evaluated their performance on data of 27 most traded cryptocurrencies using a history of previous days' sentiment data along with close prices as input data. The flexible input layer of our model enables different ways of feeding data into the model to adjust it for different cryptocurrencies to obtain better predictions. Our analysis revealed several important findings. We showed that longer sequences of input data achieve most accurate predictions on average. More specifically, using a history of 14‐ and 21‐days' data results in lowest RMSE values on average compared to using a history of 7 days. However, there is no significant difference between the results related to the input sequences with lengths of 14 and 21. In addition, our findings suggest that sentiment data can be useful in predicting prices for more than 70% of the studied cryptocurrencies. Thus, peoples' emotions, opinions, and sentiment that are expressed through their posts on Twitter platform play a significant role in prediction of cryptocurrencies' prices. [ABSTRACT FROM AUTHOR]

Collaboratively established talk rules, combined with teacher scaffolding, can help students talk together in ways that support learning. Most classrooms today utilise digital technology, and talk rules have proven beneficial for learning in this context. However, research on the strategies teachers use to make talk rules relevant in activities that include digital technology is rare. The authors analyse teachers' strategies in 57 lessons, part of a teacher professional development initiative featuring explicit focus on talk rules and teachers' use of the microblogging tool Talkwall. Their results contribute to understanding (1) how teachers can use digital technology to make talk rules relevant in activities and (2) how digital technology, when used with a dialogic approach, can promote student talk for learning. Lastly, they consider next steps in creating productive resources for teacher professional development focused on digital technology. [ABSTRACT FROM AUTHOR]

In recent years, research on opinion mining from X (formerly Twitter) has rapidly advanced, focusing on processing tweets to determine user sentiments about events. Many researchers prefer using machine and deep learning techniques for this analysis. This work proposes a novel approach integrating the C4.5 procedure, fuzzy rule patterns, and convolutional neural networks. The approach involves six steps: pre-processing to remove noisy data, vectorizing tweets with word embedding, extracting sentiment and contextual features using convolutional neural networks, fuzzifying outputs with a Gaussian fuzzifier to handle ambiguity, constructing a fuzzy tree and rule base using a fuzzy version of C4.5, and classifying tweets with fuzzy General Reasoning. This method combines the benefits of convolutional neural networks and C4.5 while addressing imprecise data with fuzzy logic. Implemented on a Hadoop framework-based cluster with five computing units, the approach was extensively tested. The results showed that the model performs exceptionally well on the COVID-19_Sentiments dataset, surpassing other classification algorithms with a precision rate of 94.56%, false-negative rate of 5.28%, classification rate of 95.15%, F1-score of 94.63%, kappa statistic of 95.12%, execution time of 11.81 s, false-positive rate of 4.26%, error rate of 4.26%, specificity of 95.74%, recall of 94.72%, stability with a mean deviation standard of 0.09%, convergence starting around the 75th round, and significantly reduced complexity in terms of time and space. [ABSTRACT FROM AUTHOR]

This study explores how European think tanks with obstructive positions on climate policy use the social network X to advance their agendas. The aim is to understand their digital communication strategies, the issues they address, the use of hyperlinks, and the impact on interaction and online polarization. A mixed-methods analysis was conducted on tweets from twelve organizations known for opposing climate policies. Out of an initial 96,607 tweets, 803 relevant messages were selected to evaluate thematic content and interaction reach. The analysis identified five dominant thematic areas in the tweets: economic impacts of climate policy, ideological perspectives, questioning of official science, proposed technological solutions, and other messages. The higher levels of interaction were generated by messages with a political or ideological focus and those proposing technological solutions. In addition, most hyperlinks directed users to the think tanks' own websites rather than to external sources. European anti-climate change think tanks use social network X to promote their agendas through ideological and technical messages that generate high engagement. [ABSTRACT FROM AUTHOR]

This study explores the phenomena of group polarization and echo chambers within the context of online discussions among immigrants in Japan, also known as gaijins, specifically within the #GaijinTwitter community. By analyzing the key topics discussed by divergent groups of Twitter users and examining their interactions through qualitative and quantitative approaches, we provide evidence of group polarization. Additionally, we investigate how blocking and sharing screenshots of tweets instead of reacting to them in the standard ways contribute to the formation and perpetuation of online echo chambers. [ABSTRACT FROM AUTHOR]

Aspirational vice presidents need to continuously support the president yet still make a name for themselves for future elections. To understand how US VP Kamala Harris attempted to communicatively enact this balance, this study content‐analyzed Harris's official VP and personal Twitter/X accounts leading up to and following the 2022 midterms to determine how she shifted her message emphases before and after the elections and across officeholder and personal accounts. Results indicate she played her "party role" pre‐midterms and "executive branch role" post‐midterms, championed Joe Biden and Democrats but not herself, and used her personal account for campaigning. [ABSTRACT FROM AUTHOR]

This research utilizes the theoretical framework of the protest paradigm to analyze how major TV channels and newspapers in Chile and Colombia covered—on their official X (Twitter) accounts—the massive 2019 protests. The paper collected data using the software Crimson Hexagon (CH), a social media analysis software that accesses all public messages posted on Twitter, and then conducted a manual content analysis to fully explore the adherence to the paradigm in digital environments, including audience interactions with media content. Results show that chosen media outlets take mainly the riot and confrontation frames to delegitimize protesters, partially influencing the reaction of audiences who engaged with those diminishing devices. A further analysis demonstrates how deeply intertwined the media are with the status quo and elites. In addition, legacy media, particularly TV, seem to fall into a systematic delegitimization of social protest. This research is valuable as it enhances the understanding of media portrayals of protests in Latin America given the new dynamics of news engagement on social media. [ABSTRACT FROM AUTHOR]

ChatGPT, a sophisticated chatbot system by OpenAI, gained significant attention and adoption in 2022 and 2023. By generating human-like conversations, it attracted over 100 million monthly users; however, there are concerns about the social impact of ChatGPT, including panic, misinformation and ethics. Twitter has become a platform for expressing views on ChatGPT and popular NLP approaches like topic modelling, sentiment analysis and emotion detection are commonly used to study public discourses on Twitter. While these approaches have limitations, an analytical process of existing best practices captures the evolving nature of these views. Previous studies have examined early reactions and topics associated with ChatGPT on Twitter but have not fully explored the combination of topics, sentiment and emotions, nor have they explicitly followed existing best practices. This study provides an overview of the views expressed on Twitter about ChatGPT by analysing 88,058 tweets from November 2022 to March 2023 to see if panic and concern were replicated in Twitter discourses. The topics covered human-like text generation, chatbot development, writing assistance, data training, efficiency, impact on business and cryptocurrency. Overall, the sentiment was predominantly positive, indicating that concerns surrounding ChatGPT were not widely replicated. However, sentiment fluctuated, with a decline observed around the launch of ChatGPT Plus. The discourse saw consistent patterns of trust and fear, with trust maintaining a steady presence until a decline potentially influenced by concerns about biases and misinformation. We discuss how our findings build upon existing research regarding ChatGPT by providing trajectories of topics, sentiment and emotions. [ABSTRACT FROM AUTHOR]

In the digital era, social media has become essential to users' daily lives, providing platforms such as Facebook, Twitter, Instagram, and others with unprecedented opportunities for interaction and information exchange. However, these platforms expose users to many security threats, such as identity theft, phishing, data breaches, and unauthorized access to personal accounts. This study systematically reviews social media security threats and highlights AI-based solutions to mitigate these risks. After conducting extensive research in scientific databases such as (Institute of Electrical and Electronics Engineers) Explore, (Association for Computing Machinery) Digital Library, Science Direct, and others, more than 1000 relevant research papers were found. After applying specific filtering criteria, the number of studies was reduced to 160 and then to 42 papers analyzed in depth. The research uses AI approaches, including machine learning, deep learning, natural language, etc., to detect anomalous behaviors and malicious intent in social media interactions. The findings provide a broad overview of the different research areas and their interrelationships. It also highlights areas that may benefit from increased research attention to enhance the security of this platform. The research found that early detection conserves and adds security benefits, and AI was important for this effort--phishing, data breaches, etc. The current study laid the foundation for future related studies focusing on security models and how they can be improved, emphasizing the need to strike a balance between protecting privacy and ensuring security. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Sciences Research / Sosyal Bilimler Arastirmalari Dergisi is the property of ODU Journal of Social Sciences Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social media is a medium that is often used by someone to express themselves. These various problems on social media have encouraged research in sentiment analysis to become one of the most popular research fields. Various methods are used in sentiment analysis research, ranging from classic machine learning (ML) to deep learning. Researchers nowadays often use deep learning methods in sentiment analysis research because they have advantages in processing large amounts of data and providing high accuracy. However, deep learning also has limitations on the longer computational side due to the complexity of its network architecture. Knearest neighbor (KNN) is a robust ML method but does not yet provide high-accuracy results in multi-lingual sentiment analysis research, so a hyperparameter tuning KNN approach is proposed. The results showed that using the proposed method, the accuracy level improved to 98.37%, and the classification error (CE) improved to 1.63%. The model performed better than other ML and even deep learning methods. The results of this study indicate that KNN using hyperparameter tuning is a method that contributes to the sentiment analysis classification model using the Twitter dataset. [ABSTRACT FROM AUTHOR]

This study examined 106,979 tweets posted by 34,370 unique users from 2010 to 2019 to understand the sentiment of those indirectly involved in orphanage tourism and its change over time. The result from this study indicated that despite recent critiques of orphanage tourism, the conversations on orphanage volunteering were still largely positive. The persistence of positive sentiment toward orphanage tourism was due to fleeting critiques, the low rate of new critics joining the discourse, and the limited engagement of the existing users. The findings implied that there is a need for further anti-orphanage tourism campaigns and active recruitment of orphanage tourism critiques. The current study contributed to the limited literature on orphanage tourism by utilizing social media to extract valuable information about the perceptions of not only the directly involved stakeholders but also those indirectly involved. [ABSTRACT FROM AUTHOR]

Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic caused fear and uncertainty about vaccines, which has been well expressed on social media platforms such as Twitter (X). We analyse sentiments from the beginning of the COVID-19 pandemic and study the public behaviour on X during the planning, development, and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. We provide visualisation and analysis of anti-vaccine sentiments throughout the COVID-19 pandemic. We review the nature of the sentiments expressed with the number of tweets and monthly COVID-19 infections. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19. We also find that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised, implying that the vaccine rollout impacted the nature of discussions on social media. [ABSTRACT FROM AUTHOR]

This study examines water contamination issues using social media data (n = 420.7 k) to unveil the roles and patterns from the top 10 most influential users in their respective networks determined by their reply, retweet, and mention networks. Findings from RQ1 show diverse entities within this group, encompassing political figures, organizations, cause-related actors (e.g., EPA), educational institutions, and high-activity accounts all contribute to the discourse on water contamination. While RQ2 sought to examine the evolution of discourse regarding water contamination and its related dynamics, instead, communication "shocks", or deviations from typical discourse that returned to the original state, were identified, rather than revolutionary moments on social media that triggered long-term changes. [ABSTRACT FROM AUTHOR]

Social media users often express their emotions through text in posts and tweets, and these can be used for sentiment analysis, identifying text as positive or negative. Sentiment analysis is critical for different fields such as politics, tourism, e-commerce, education, and health. However, sentiment analysis approaches that perform well on English text encounter challenges with Arabic text due to its morphological complexity. Effective data preprocessing and machine learning techniques are essential to overcome these challenges and provide insightful sentiment predictions for Arabic text. This paper evaluates a combined CNN-LSTM framework with emoji encoding for Arabic Sentiment Analysis, using the Arabic Sentiment Twitter Corpus (ASTC) dataset. Three experiments were conducted with eight-parameter fusion approaches to evaluate the effect of data preprocessing, namely the effect of emoji encoding on their real and emotional meaning. Emoji meanings were collected from four websites specialized in finding the meaning of emojis in social media. Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. The highest accuracy rate (91.85%) was achieved by keeping non-Arabic words and removing punctuation, using the Snowball stemmer after encoding emojis into Arabic text, and applying Keras embedding. This approach is competitive with other state-of-the-art approaches, showing that emoji encoding enriches text by accurately reflecting emotions, and enabling investigation of the effect of data preprocessing, allowing the hybrid model to achieve comparable results to the study using the same ASTC dataset, thereby improving sentiment analysis accuracy. [ABSTRACT FROM AUTHOR]

This study analyzed 2421 microblogs posted between the year 2012 to March 2022 reflecting the microbloggers' attitudes toward different cultures. Results indicated that (1) the number of microblog posts expressing the users' intercultural attitudes increased distinctly from 2019 to March 2022, with females users in general posting more microblogs than males; (2) females posted more microblogs encompassing positive emotions to show their interest and motivation to learn about foreign cultures, and the tendency to value and appreciate cultural differences, whereas males created more sentimentally neutral posts that revealed their recognition of the existence of cultural differences, and females and males posted a similar number of microblogs containing negative emotions; and (3) more posts involved "small c" culture were posted than those containing themes belonging to the "Big C" culture. Gender gap was further observed regarding the cultural themes concerned by the microbloggers. Implications were discussed in the context of intercultural education. [ABSTRACT FROM AUTHOR]

Marketers frequently create social media content (i.e., firm-generated content; FGC) to ignite interest in new movies. Thus, there is a clear need to understand the magnitude and heterogeneity of the effect of FGC on movie demand and associated user-generated content (UGC). The authors empirically examine the complex interactions among FGC, UGC, and sales using social media (tweet) data that are normally available to firms. They investigate two potential mechanisms by which FGC may drive box office revenues: (1) a direct mechanism, such that users who see FGC directly drive revenue, and (2) an indirect "ripple effect," by which FGC increases movie-related UGC, which then drives consumption. By analyzing 145,502 firm-generated and 5.9 million user-generated Twitter posts associated with 159 movies, the authors find a positive and significant effect of FGC on movie sales, which UGC fully mediates, which supports the indirect ripple effect reasoning. Impressions of FGC by followers of firm accounts, as opposed to nonfollowers of firm accounts, mainly drive the effect of FGC on UGC. In addition, FGC by movie accounts is more effective than that by actors and studios. Firms' regular posts with a movie-specific hashtag are more effective than replies, retweets, and posts without the hashtag. The finding of the ripple effect suggests that movie executives should focus on creating FGC that sparks conversations among followers when new movies are released. [ABSTRACT FROM AUTHOR]

With a growing number of people using social media such as Instagram, Facebook, and Twitter, it has become extremely important for professional athletes to build and promote their personal brands through social media. The purpose of this study was to understand how LPGA Tour Korean golfers use social media for self-presentation. Through content analysis, the self-presentation forms of the top six Korean LPGA Tour golfers were examined. The result showed that the golfers are more likely to use the form of the front stage rather than the backstage. The number of likes and comments is higher when golfers post backstage photos and write photo stories in both Korean and English languages on Instagram. This study contributes to the field of sport social media research theoretically with new subcategorization to Goffman's self-presentation and suggests a new insight into personal brand marketing strategies via social media for both athletes and sponsors. [ABSTRACT FROM AUTHOR]

In 2019, a controversy between the NBA and China broke out. Although their relationship continues today, negative consequences still linger and cloud their future. As a transnational organization, the NBA was involved in a cross-national controversy, aligning with the theory of cross-national conflict shifting. The current study analyzed 703 posts on Weibo and 1,500 tweets by thematic analysis. It revealed diverse themes of online public discussions regarding the NBA–China controversy. It also found that social media speeded up the transmission of cross-national conflict shifting and complicated the cross-national conflict as it shifted back and forth between the home and host countries. Moreover, the study findings showed that when top executives engage in advocacy by taking a public stand on a controversial sociopolitical issue and get involved with cross-national conflicts, it is hard to separate them from the organization they represent. Also, their public stance might lead to public suspicion that they used social advocacy for private interests. Finally, the themes from the social media posts suggested cultural differences and an ideology crash between the host and home countries' publics, which generated grander challenges for transnational organizations to deal with. [ABSTRACT FROM AUTHOR]

K-12 computer science (CS) education has emerged as a vital component of modern education, nurturing computational thinking, problem-solving, and digital literacy. This study examines the K-12 CS education dynamics, emphasizing its impact and implications, particularly in the context of equity. Twitter data from 2017 to 2021 were collected, focusing on English-language tweets within the United States. This collection was completed before Elon Musk’s acquisition of the company and its subsequent rebranding to X. Three keyword sets span CS education, computational thinking – a core competency of CS learners and CS education organizations and conferences. The findings indicate: (1) a significant decrease in tweet volumes for each set of keywords after 2019, (2) the critical role of coding within a broader STEM education framework, and (3) the centrality of students in semantic networks formed by the tweets, highlighting the pertinence of a student-centered learning strategy in K-12 CS education. To ensure equitable access and opportunities, K-12 CS education in a broader STEM ecosystem should adopt student-centered learning, with teachers facilitating coding, programming, and technology education. These insights inform educators, policymakers, and researchers about K-12 CS education’s significance in preparing students for the future, with a strong emphasis on equity and inclusion. [ABSTRACT FROM AUTHOR]

This article offers a discursive analysis of conflictive interactions on Twitter (X) about the attribution of responsibility for human rights abuses in a Chilean case study, namely the death of a man after being run over by Carabineros on January 29, 2020. This event provoked many reactions on social media concerning the attribution of responsibility, a recurrent issue in contexts of human rights abuses. The corpus is composed of three headlines published on Twitter by the television channels/programmes Teletrece, CNN Chile and 24 Horas TVN, and their comments (436 tweets analysed). In order to understand how users react to these tweets and interact to defend their point of view and manage the conflict, we analyse agentivity, social actors, attenuation and intensification. The analysis shows that users conceptualise responsibility in different ways according to whether they support the victim or the perpetrators. Moreover, the conceptualisation of responsibility varies between the three media analysed and attenuation is only found when the user interacts with another user. [ABSTRACT FROM AUTHOR]

Social media allow citizens to express their opinions on all aspects of life and society, and this trend has been enhanced during the COVID-19 crisis, when more "traditional" ways of opinion expression were not possible. To get the feeling of Twitter users' opinions on topics of importance we analysed tweets and combined them with relevant news, thus allowing for potential event detection. We showcase the prototypical framework that we have developed with our findings about European COVID-19 mobile contact tracing apps in tweets posted between 09/07/2020 and 10/07/2021. We obtained both high-level results (for example, trending twitter activity, sentiment polarisation of important hashtags, etc.) and more specific ones (such as, the spatial distribution of tweets regarding a specific app), which indicate that our approach can be applied in the future to get useful insights on topics of public interest that result in active discussions on social media platforms. [ABSTRACT FROM AUTHOR]

This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 2023). This period was selected, coinciding with the rise of artificial intelligence (AI) tools, particularly ChatGPT. The study investigates the discussions, emotional tones, and dominant topics shaping the global narrative of higher education within X (Twitter) data. Key findings include the geographical distribution of tweets and the most frequent positive and negative perceptions. It also addresses critical issues such as affordability, accessibility, and funding in higher education. Furthermore, the data shows public reactions to AI in higher education are initially negative, while higher education tweets are primarily characterized by positivity and optimism. The higher education tweets are mainly posted on the weekend, with decreased activity during weekdays. This research provides insights into the evolving higher education landscape amid rapid technological advancements. [ABSTRACT FROM AUTHOR]

While social media platforms promote people's information exchange and dissemination, they also make rumors spread rapidly on online platforms. Therefore, how to detect rumors quickly, timely and accurately has become a hot topic for scholars in related fields. Traditional deep learning models ignore the relationship and topology between nodes in the rumor detection task and use fixed weights or mean aggregation strategies in the feature aggregation process, which fail to capture the complex interactions between nodes and the dynamics of information propagation, limiting the accuracy and robustness of the rumor detection model. To address these problems, we propose a location-aware weighted GraphSAGE rumor detection model GSMA. We first introduce an attention mechanism that dynamically assigns different attention weights to different neighboring nodes for different degrees of aggregation, improving GraphSAGE's strategy of using mean-value aggregation for all neighboring nodes during the aggregation process; second, we introduce a modulated position encoding into the model and encode the position information of nodes into the features to improve the model's ability to perceive the relative position and order of nodes; finally, the post text sentiment is incorporated into the features to provide additional semantic information for the model as a way to achieve rumor detection in microblogging platforms. Experiments show that the accuracy of the GSMA model on Ma-Weibo and Weibo23 reaches 97.43% and 97.55%, which is an improvement of 1.11% and 0.77% compared to the benchmark GraphSAGE, and all the evaluation metrics are also improved compared to other optimal rumor detection models. [ABSTRACT FROM AUTHOR]

The advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for Twitter sentiment data using word count vectorization and machine learning techniques to reduce the difficulties faced with annotated sentiment-labelled tweets. Various classifiers (Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)) were evaluated based on accuracy, precision, recall, F1-score, and specificity. Random Forest outperformed the others with an Area under Curve (AUC) value of 0.96 and an average precision (AP) score of 0.96 in sentiment classification, especially effective with minimal Twitter-specific features. [ABSTRACT FROM AUTHOR]

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. [ABSTRACT FROM AUTHOR]

Weibo is an event-focused microblogging platform open to the public, easily triggering users' social media fatigue. Based on the Stressor – Strain – Outcome (S-S-O) model, this research proposes a path model to explore the antecedents and consequences of social media fatigue among Chinese Weibo users. Findings from an online survey of 414 Weibo users reveal that caused of fear of missing out (FoMO), they might experience social media fatigue through information overload, further causing them to develop discontinuous usage intentions. This study contributes to the literature on Chinese social media platforms and their cultural-specific nuances. Theoretical insights for future research are provided, and practical suggestions are recommended for Weibo users. [ABSTRACT FROM AUTHOR]

Sentiment is employed in various fields, such as collecting web-based opinions for the formulation of governmental policies, measuring employee and customer satisfaction levels in business organizations, and measuring the sentiment of the public in political and security matters. The field has recently faced new challenges since algorithms must operate with highly unstructured sentiment data from social media. In this study, the authors present a new stacking ensemble method that combines the lexicon-based approach with machine learning algorithms to improve the sentiment analysis of tweets. Due to the complexity of the text with very ill-defined syntactic and grammatical patterns, using lexicon-based techniques to extract sentiment from the content is proposed. On the same note, the contextual and nuanced aspects of sentiment are inferred through machine learning algorithms. A sophisticated bat algorithm that uses an Elman network as a meta-classifier is then employed to classify the extracted features accurately. Substantial evidence from three datasets that are readily available for public analysis re-affirms the improvements this innovative approach brings to sentiment classification. [ABSTRACT FROM AUTHOR]

Amid concerns over algorithmic gatekeeping and the power of digital platforms to serve as engines for polarization and disinformation, we examined the performance of algorithmic recommendation systems as news intermediaries by crowdsourcing search results about newsworthy topics. This study offers a crowdsourced audit of the recommendations made by Google, Google News, Facebook, YouTube, and Twitter to ideologically, geographically, and demographically diverse U.S. participants (N = 1,598), examining the extent to which search algorithms on major platforms personalized results and drove traffic to particular kinds of websites. The findings of our cross-platform analysis show that rather than creating filter bubbles, the sorting mechanisms on platforms strongly homogenize exposure to information, at least among its top results. This effect was evident across search terms and platforms. At the same time, each platform prioritizes different types of content, with professionally produced news dominant on some platforms but not others, and politically conservative mainstays like Fox News being particularly recurrent. [ABSTRACT FROM AUTHOR]

In this paper, we propose TAWE (title assignment with word embedding), a new method to automatically assign titles to topics inferred from sets of documents. This method combines the results obtained from the topic modeling performed with, e.g., latent Dirichlet allocation (LDA) or other suitable methods and the word embedding representation of words in a vector space. This representation preserves the meaning of the words while allowing to find the most suitable word that represents the topic. The procedure is twofold: first, a cleaned text is used to build the LDA model to infer a desirable number of latent topics; second, a reasonable number of words and their weights are extracted from each topic and represented in n-dimensional space using word embedding. Based on the selected weighted words, a centroid is computed, and the closest word is chosen as the title of the topic. To test the method, we used a collection of tweets about climate change downloaded from some of the main newspapers accounts on Twitter. Results showed that TAWE is a suitable method for automatically assigning a topic title. [ABSTRACT FROM AUTHOR]

Previous scholarship has identified three levels of journalists' brand identity: individual, organizational, and institutional. Based on an exploration of Israeli journalists on the Hebrew-speaking Twittersphere during three consecutive election campaigns (2019–2021), this paper wishes to offer an additional level: Ideological Branding. Such branding is based on journalists' ideological, political, social, economic, and religious viewpoints as well as their biography and networks of friends. It functions as a bridge that connects and integrates the professional and personal levels. The study employed a mixed-method approach, including in-depth interviews and quantitative and qualitative content analysis of 10 prominent Israeli journalists' Twitter profiles and political tweets (N = 2,758). The findings reveal that journalists, in general, and conservative ones in particular, are normalizing their political messages as legitimate journalistic narratives within mainstream media by effectively leveraging their ideological standpoint on Twitter to ignite online viral activity, thereby enhancing their brand recognition, career prospects, and celebrity status. [ABSTRACT FROM AUTHOR]

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The main purpose of this study was to provide a unified framework of key stakeholder action-oriented approaches to extended producer responsibility based on behavioral estimates of consumers. For Twitter data, descriptive analysis and content analysis (thematic modeling) were performed after reviewing the literature. In addition, a new text analysis framework was employed to analyze extended producer responsibility reports. A total of 2,561,216 tweets and 708 documents related to producer responsibilities were analyzed. According to descriptive analysis, tweets related to North American countries represent about 35 percent of Twitter activity, while tweets related to African countries represent about 5 percent. A further consideration is the amount of attention paid by policymakers to issues related to mobile phones and environmental concerns among young people (about 5% in the 10–20 age group and 15% in the 20–30 age group). Consumer participation is significantly influenced by social factors such as education, awareness, and trust. In addition, consumers are increasingly concerned about data security and privacy. Also, cost, economic benefits, proximity, and convenience influenced consumer participation. The second part of the analysis revealed that producers are attending to enhance the economic benefits of recovery, develop infrastructure (for consumer convenience), ensure data security and privacy for participants, and increase knowledge and awareness about recycling and recovery. The developed countries also work with producers to increase engagement, while developing countries are trying to overcome information transparency, a lack of economic potential, and public awareness. [ABSTRACT FROM AUTHOR]

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