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Introduction: The COVID-19 pandemic caused a widespread public health and financial crisis. The rapid vaccine development generated extensive discussions in both mainstream and social media, sparking optimism in the global financial markets. This study aims to explore the key themes from mainstream media's coverage of COVID-19 vaccines on Facebook and examine how public interactions and responses on Facebook to mainstream media's posts are associated with daily stock prices and trade volume of major vaccine manufacturers., Methods: We obtained mainstream media's coverage of COVID-19 vaccines and major vaccine manufacturers on Facebook from CrowdTangle, a public insights tool owned and operated by Facebook, as well as the corresponding trade volume and daily closing prices from January 2020 to December 2021. Structural topic modelling was used to analyze social media posts while regression analysis was conducted to determine the impact of Facebook reactions on stock prices and trade volume., Results: 10 diverse topics ranging from vaccine trials and their politicization (note: check that we use American spelling throughout), to stock market discussions were found to evolve over the pandemic. Although Facebook reactions were not consistently associated with vaccine manufacturers' stock prices, 'Haha' and 'Angry' reactions showed the strongest association with stock price fluctuations. In comparison, social media reactions had little observable impact on trading volume., Discussion: Topics generated reflect both actual events during vaccine development as well as its political and economic impact. The topics generated in this study reflect both the actual events surrounding vaccine development and its broader political and economic impact. While we anticipated a stronger correlation, our findings suggest a limited relationship between emotional reactions on Facebook and vaccine manufacturers' stock prices and trading volume. We also discussed potential technical enhancements for future studies, including the integration of large language models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bai and Lee.)

Social media provide easy ways to autistic individuals to communicate and to make their voices heard. The objective of this paper is to identify the main themes that are being discussed by autistic people on Twitter. We collected a sample of tweets containing the hashtag #ActuallyAutistic during the period 10/02/2022 and 14/09/2022. To identify the most discussed topics, BERTopic modelling was applied. We manually grouped the detected topics into 6 major themes using inductive content analysis: 1) General aspects of autism and experiences of autistic individuals; 2) Autism awareness, pride and funding; 3) Interventions, mostly related to Applied Behavior Analysis; 4) Reactions and expressions; 5) Everyday life as an autistic (lifelong condition, work, housing…); and 6) Symbols and characteristics. The majority of tweets were presenting general aspects and experiences as autistic individuals; raising awareness; and about their dissatisfaction with some interventions. The identification of autistic individuals' main discussion themes could help to develop meaningful public health agendas and research involving and addressed to autistic individuals.

Background: Social media is an important medium for studying public attitudes toward COVID-19 vaccine mandates in Canada, and Reddit network communities are a good source for this., Methods: This study applied a "nested analysis" framework. We collected 20378 Reddit comments via the Pushshift API and developed a BERT-based binary classification model to screen for relevance to COVID-19 vaccine mandates. We then used a Guided Latent Dirichlet Allocation (LDA) model on relevant comments to extract key topics and assign each comment to its most relevant topic., Results: There were 3179 (15.6%) relevant and 17199 (84.4%) irrelevant comments. Our BERT-based model achieved 91% accuracy trained with 300 Reddit comments after 60 epochs. The Guided LDA model had an optimal coherence score of 0.471 with four topics: travel, government, certification, and institutions. Human evaluation of the Guided LDA model showed an 83% accuracy in assigning samples to their topic groups., Conclusion: We develop a screening tool for filtering and analyzing Reddit comments on COVID-19 vaccine mandates through topic modelling. Future research could develop more effective seed word-choosing and evaluation methods to reduce the need for human judgment.

Many studies have forewarned the profound emotional and psychosocial impact of the protracted COVID-19 pandemic. This study thus aimed to examine how individuals relate to suicide amid the COVID-19 pandemic from a global perspective via the public Twitter discourse around suicide and COVID-19. Original Twitter tweets from 1 February 2020 to 10 February 2021 were searched, with terms related to "COVID-19", "suicide", or "self-harm". An unsupervised machine learning approach and topic modelling were used to identify topics from unique tweets, with each topic further grouped into themes using manually conducted thematic analysis by the study investigators. A total of 35,904 tweets related to suicide and COVID-19 were processed into 42 topics and six themes. The main themes were: (1) mixed reactions to COVID-19 public health policies and their presumed impact on suicide; (2) biopsychosocial impact of COVID-19 pandemic on suicide and self-harm; (3) comparing mortality rates of COVID-19, suicide, and other leading causes of death; (4) mental health support for individuals at risk of suicide; (5) reported cases and public reactions to news related to COVID-19, suicide, and homicide; and (6) figurative usage of the word suicide. The general public was generally concerned about governments' responses as well as the perturbing effects on mental health, suicide, the economy, and at-risk populations.

Objective: This study aimed to explore topics and sentiments using tweets from Ontario, Canada, during the second wave of the COVID-19 pandemic. Methods: Tweets were collected from December 5, 2020, to March 6, 2021, excluding non-individual accounts. Dates of vaccine-related events and policy changes were collected from public health units in Ontario. The daily number of COVID-19 cases was retrieved from the Ontario provincial government's public health database. Latent Dirichlet Allocation was used for unsupervised topic modelling. VADER was used to calculate daily and average sentiment compound scores for topics identified. Results: Vaccine, pandemic, business, lockdown, mask, and Ontario were six topics identified from the unsupervised topic modelling. The average sentiment compound score for each topic appeared to be slightly positive, yet the daily sentiment compound scores varied greatly between positive and negative emotions for each topic. Conclusion: Our study results have shown a slightly positive sentiment on average during the second wave of the COVID-19 pandemic in Ontario, along with six topics. Our research has also demonstrated a social listening approach to identify what the public sentiments and opinions are in a timely manner., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tsao, MacLean, Chen, Li, Yang and Butt.)

Few studies have mined social media platforms to assess environmental concerns. In this study, Twitter was scraped to obtain a ~140,000 tweet dataset related specifically to marine plastic pollution. The goal is to understand what kind of users profiles are tweeting and how and when they do it. In addition, topic modelling and graph theory techniques have allowed us to identify main concerns on this topic: i) impact on wildlife, ii) microplastics/water pollution, iii) estimates/reports, iv) legislation/protection, and v) recycling/cleaning initiatives. Results reveal a scarce influence of organizations involved in research and marine environmental awareness, so some guidelines are depicted that could help to adjust their communication plans. This is relevant to engage society through reliable information, change habits and reinforce sustainable behaviour. A visualization tool has been created to analyze the results over time., (Copyright © 2021. Published by Elsevier Ltd.)

Background and Objective: Social media could be valuable tools to support people with multiple sclerosis (MS). There is little evidence on the MS-related topics that are discussed on social media, and the sentiment linked to these topics. The objective of this work is to identify the MS-related main topics discussed on Twitter, and the sentiment linked to them., Methods: Tweets dealing with MS in the English language were extracted. Latent-Dirilecht Allocation (LDA) was used to identify the main topics discussed in these tweets. Iterative inductive process was used to group the tweets into recurrent topics. The sentiment analysis of these tweets was performed using SentiStrength., Results: LDA' identified topics were grouped into 4 categories, tweets dealing with: related chronic conditions; condition burden; disease-modifying drugs; and awareness-raising. Tweets on condition burden and related chronic conditions were the most negative (p<0.001). A significant lower positive sentiment was found for both tweets dealing with disease-modifying drugs, condition burden, and related chronic conditions (p<0.001). Only tweets on awareness-raising were most positive than the average (p<0.001)., Discussion: The use of both tools to identify the main discussed topics on social media and to analyse the sentiment of these topics, increases the knowledge of the themes that could represent the bigger burden for persons affected with MS. This knowledge can help to improve support and therapeutic approaches addressed to them.

Background: Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field., Methods: With the basis of 4361 publications from both Web of Science and PubMed during the year 2008-2017, the analysis utilizes methods including topic modelling and science mapping analysis., Results: Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected., Conclusions: This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction.

Sentiment analysis is a process of computationally finding the opinions that are expressed in a short text or a feedback by a writer towards a particular topic, product, service. The short piece of review from the user can help a business determine or understand the attitude of the user thereby predict the customer's behaviour and itsubstantiallyimproves the quality of service parameters. The proposed Robust User Sentiment Biterm Topic Mixture (RUSBTM)model discovers the user preference and their sentiment orientation views for effective Topic Modelling using Biterms or word-pair from the short text of a particular venue. Since short review or text suffers from data sparse, the user aggregation strategy is adapted to form a pseudo document and the word pairset is created for the whole corpus. The RUSBTM learns topics by generating the word co-occurrence patterns thereby inferring topics with rich corpus-level information. By analysing the sentiments of the paired words and their corresponding topics in the review corpus of the particular venue, prediction can be done that exactly portrays the user interest, preference and expectation from a particular venue. The RUSBTM model proved to be more robust and also, the extracted topics are more coherent and informative. Also the method uses accurate sentiment polarity techniques to exactly capture the sentiment orientation and the model proves to be outperforming better when compared to other state of art methods.

Background: In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes., Objective: Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter., Methods: The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines., Results: We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy., Conclusions: The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines., Competing Interests: Conflicts of Interest: None declared.

Opposing social movements are groups that have conflicting objectives on a shared social justice issue. To maximize the probability of their movement's success, groups can strategically portray their group in a favourable manner while discrediting their opposition. One such approach involves the construction of victimization discourses. In this research, we combined topic modelling and critical discursive psychology to explore how opposing groups within the feminist movement used victimization as a lens to understand their movements in relation to transgender women. We compiled a dataset of over 40,000 tweets from 14 UK‐based feminist accounts that included transgender women as women (the pro‐inclusion group) and 13 accounts, that excluded transgender women (the anti‐inclusion group). Our results revealed differences in how victimization was employed by the opposing movements: pro‐inclusion groups drew on repertoires that created a sense of shared victimhood between cisgender women and transgender women, while anti‐inclusion groups invoked a competitive victimhood repertoire. Both groups also challenged and delegitimised their oppositions' constructions of feminism and victimhood. These findings add to our understanding of the communication strategies used by opposing movements to achieve their mobilization goals. [ABSTRACT FROM AUTHOR]

The study investigates the dissemination of news content from two Russian state-backed media outlets, RT and Sputnik, within digital alternative news environments in Austria, Denmark, Germany, and Sweden. We investigate these media outlets as parts of a Russian "sharp power" strategy, aimed at establishing informational influence within Western news environments. The analysis includes over 3.4 million social media posts containing URLs to RT and Sputnik content, published between January 2019 and March 2022. Drawing on a combined network analytical and computational text analysis approach, we examine the dissemination of RT and Sputnik content across a range of social media platforms (Twitter/X, Facebook, Telegram, VKontakte, Gab, Instagram, Reddit, 4chan, and YouTube). In the Danish and Swedish environments, the dissemination of content into core domestic alternative news communities is limited, primarily taking place on Facebook and, to a lesser extent, on Twitter/X. Fringe platforms such as Telegram, Gab and VKontakte facilitate a wider spread of content within domestic alternative news environments in German-speaking environments, predominantly among right-wing digital clusters. Within German-speaking alternative news environments, the shared content from RT and Sputnik primarily addresses domestic issues, such as COVID-19 policies and vaccines. In contrast, Danish- and Swedish-speaking alternative news environments emphasize foreign policy matters, including the Russian–Ukraine War, WikiLeaks, and Western military actions. These findings contribute to research on foreign state-media operations in digital information environments by focusing on both content and sharing patterns. [ABSTRACT FROM AUTHOR]

This study explores the diversity of topics in political campaign communication on social media during the 2022 Australian federal election. While political campaigns on social media are often associated with both persuasive and mobilising appeals, this research focuses on understanding the differences in persuasive content by comparing organic (non-targeted) and paid (targeted) political communication. Analysing the Australian context, which follows a Westminster system, with compulsory voting, we utilise data from the federal election 2022 to investigate how political actors employ persuasive communication strategies. Through topic modelling, we examine whether distinct themes vary in content and prevalence between organic and paid social media content disseminated by political parties and candidates. Our analysis revealed that the differences in topic diversity between paid and organic content do not seem to be substantial, despite popular concerns about higher personalisation due to advertising targeting which could lead to information fragmentation of the electorate. Both types of content predominantly focus on core political topics, aligning with party ideologies and include overall campaign information (e.g., on election procedures). However, government critique emerges as a distinct topic in both organic and paid content signalling the usage of negative campaigning to weaken opposing parties. In conclusion, this study suggests that the strategic manipulation of the electorate through social media during the Australian federal election in 2022 was limited. Nonetheless, the prevalence of negative appeals towards the government and opposing parties raises questions about the potential impact on citizens' trust in democracy and institutions. [ABSTRACT FROM AUTHOR]

Social media are increasingly used to obtain and disseminate information about environmental issues. Yet, environmental communication research has focused mainly on social media discussions pertaining to climate change, while overlooking public awareness and discourse regarding the other planetary boundaries (i.e., important and interlinked environmental issues other than climate change). Moreover, while discussions about climate change are often found to be polarising, it remains to be seen if this extends to other environmental issues. We used network analysis and topic modelling to analyse two million environment-related tweets and identified nine 'green communities' of users. Climate change was the most popular issue across all communities and other issues like biodiversity loss were discussed infrequently. The discourse was less polarised than previously assumed, was largely pro-environmental, and originated more from the Global North than the Global South. The relevance of our findings for policymakers and researchers in environmental communication is discussed. [ABSTRACT FROM AUTHOR]

IntroductionThe COVID-19 pandemic caused a widespread public health and financial crisis. The rapid vaccine development generated extensive discussions in both mainstream and social media, sparking optimism in the global financial markets. This study aims to explore the key themes from mainstream media’s coverage of COVID-19 vaccines on Facebook and examine how public interactions and responses on Facebook to mainstream media’s posts are associated with daily stock prices and trade volume of major vaccine manufacturers.MethodsWe obtained mainstream media’s coverage of COVID-19 vaccines and major vaccine manufacturers on Facebook from CrowdTangle, a public insights tool owned and operated by Facebook, as well as the corresponding trade volume and daily closing prices from January 2020 to December 2021. Structural topic modelling was used to analyze social media posts while regression analysis was conducted to determine the impact of Facebook reactions on stock prices and trade volume.Results10 diverse topics ranging from vaccine trials and their politicization (note: check that we use American spelling throughout), to stock market discussions were found to evolve over the pandemic. Although Facebook reactions were not consistently associated with vaccine manufacturers’ stock prices, ‘Haha’ and ‘Angry’ reactions showed the strongest association with stock price fluctuations. In comparison, social media reactions had little observable impact on trading volume.DiscussionTopics generated reflect both actual events during vaccine development as well as its political and economic impact. The topics generated in this study reflect both the actual events surrounding vaccine development and its broader political and economic impact. While we anticipated a stronger correlation, our findings suggest a limited relationship between emotional reactions on Facebook and vaccine manufacturers’ stock prices and trading volume. We also discussed potential technical enhancements for future studies, including the integration of large language models.

With the rise of social media platforms for marketing purposes, the central dilemma for researchers and policymakers lies in choosing effective data analysis tools to improve marketing decisions. In the academic literature, numerous articles have discussed clustering techniques for analysing social media data, from a perspective of data mining or social media marketing. However, few studies have attempted to synthesise results obtained from both perspectives. This research aims to (1) offer a structured overview of existing literature on clustering methods for marketing strategies and (2) compare three topic modelling techniques applied to extract the main topics evoked in the corpus of papers. Indeed, topic modelling emerges as a valuable tool for extracting relevant information from big data in general and more specifically from extensive scientific papers. Based on a thematic analysis, the extracted topics were classified according to the following categories: fields, marketing strategies and technologies. Results prove that latent Dirichlet allocation (LDA) is the most effective technique in this context. Furthermore, this study provides an overview of clustering techniques and technologies used for marketing strategies in studied fields. These findings help researchers and practitioners to select the best techniques and technologies for extracting marketing knowledge from big data. [ABSTRACT FROM AUTHOR]

Information science researchers are increasingly seeking to understand the utilisation of knowledge generated through scientific research outside of academia. Although the conceptual levels of knowledge utilisation are well established, our understanding of the various information practices for knowledge utilisation employed by researchers remains limited. This study identified such information practices by text-mining 6637 case studies documented under the United Kingdom's Research Excellence Framework. The results were augmented with expert judgement to develop a framework consisting of nine types based on the theoretical framework of research knowledge utilisation. Three emerging types were identified: deliberation, co-creation and foresighting. They indicate the rise of information practices leveraging social media and analytical capabilities to engage potential beneficiaries in using and realising the value of research. [ABSTRACT FROM AUTHOR]

Social media has become a platform for information diffusion, voicing concerns of existing inequalities and raising public awareness of various social and societal issues. Despite the social good, social media has become a fertile ground for spreading misinformation, hate speech and conspiracy theories. The death of George Floyd in May 2020 triggered a series of protests worldwide in support of the Black Lives Matter (BLM) movement and triggered a debate about equity, inclusion and social justice. The purpose of this study is to examine the impact of misinformation and social noise on the original intended message of BLM using data from the Twitter hashtag 'BLM'. Results from topic modelling have shown the strong presence of misinformation and social noise. Such information was most probably intended to influence, mislead and dilute the original intended message. However, despite the effort to distort the original message of BLM, results from sentiment analysis show that users' opinions of the BLM movement remained positive. [ABSTRACT FROM AUTHOR]

This study systematically reviews the research on applying social media data (SMD) in business decision-making. We applied bibliometric mapping and a Latent Dirichlet Allocation topic modelling approach to conduct a systematic literature review. Results show that research to date has uncovered that sentiment analysis and opinion mining supported businesses in observing, analysing and predicting customer behaviour in various sectors. However, descriptive and predictive analyses are prevalent, while prescriptive analyses on SMD are rare. Our analysis highlights the need for future research to shed light onto newly discovered forms of SMD increasing the accuracy of sentiment detection with concept-level analysis. [ABSTRACT FROM AUTHOR]

The simplicity to deliver an opinion about companies or institutions via social media has resulted in both positive and negative judgments. Through social media all positive and negative information will be easily found and spread. It is concerned that negative information will lead to negative public opinion. If this occurs, the company will suffer from a lack of trust, which will harm the company's reputation. Thus, to monitor uncontrolled issues, a company wants to know what topics or opinions are developing in the community. Therefore, the topic modelling using latent dirichlet allocation (LDA) is proposed to identify topics that are being discussed on social media. The findings of this study got the coherence score of 0.558 and based on the direct human judgment, the model got an average 80% correctly. The findings of this study reveal 4 topics groups that represent the corporate social media content. These findings offer information to companies about the latest topics or opinions that are currently developing in society which could provide recommendations related to decision-making on current issues thus increasing the trust and reliability towards the company. [ABSTRACT FROM AUTHOR]

This research aims to analyse the online service quality in Indonesia's banking sector. We will carry out a consumer sentiment analysis on the BTN Mobile application, which Bank Tabungan Negara (BTN) uses to provide online services to consumers. Data mining will be done to obtain consumer review data on the Google Play Store. The target amount of data is a maximum of 500 data. The data will then be analysed to produce word clouds, topic modelling, and consumer sentiment and to produce dimensions that influence service quality. Analysis was carried out using the Orange Data Mining Application. The analysis results show dimensions that receive positive, negative and neutral sentiment from BTN Mobile users in Indonesia. [ABSTRACT FROM AUTHOR]

Global warming refers to the long-term heating of the earth, including melting of the glaciers and the expected rise in seawater level that could threaten a large number of areas and communities around the world. Social media has a big influence on people's perception and understanding of environmental issues and challenges. In this study, we examine the impact of misinformation and social noise on people's perception of global warming during critical environmental events. The study used data analytic techniques such as sentiment analysis and topic modelling to assess the level of objectivity and subjectivity in the discussion about global warming and examine the presence of certain topics that fit the definition of social noise and misinformation. Data collection was limited to the hashtag "global warming" captured during the months there was a spike in global warming tweets during the period from 2012-2022. The results from the sentiment analysis have shown a 62.46% subjectivity score compared to 37.54% objectivity score, indicating that most of the tweets posted were based on users' personal opinion and perception of global warming. Topic modelling showed a prevalence of social noise in the tweets indicating the presence of social noise and thus aids in understanding the factors that lead to misinformation creation. The presence of social noise questions the credibility of the tweets and its contents. [ABSTRACT FROM AUTHOR]

Product recall crises can damage reputation, result in financial losses, and generate negative word-of-mouth from customers. Appropriate response strategies on social media platforms can help companies minimise losses and save electronic word-of-mouth (eWOM). This study analysed 32,697 customer comments from Facebook to explore customer perceptions and examine the performance of different response strategies. Findings showed that the accommodative response strategy improves overall eWOM compared to the indifferent response strategy. The study provides a comprehensive understanding of the impact of social media crisis response strategies on eWOM and offers evidential managerial insights for better customer perceptions. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression. Design/methodology/approach: This research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM). Findings: The study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics. Practical implications: For hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice. Originality/value: The study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic. [ABSTRACT FROM AUTHOR]

The term 'grey zone' indicates a state actor's actions up to the point of armed conflict and is increasingly associated with China's foreign policy. China has harnessed Western social media to defend its national interests, drawing international attention to its discourse of war and its often-hostile rhetoric. This paper analyses Chinese state-sponsored tweets about Taiwan, a focal point for Chinese grey zone activity. Empirical topic modelling techniques to aggregate narratives in large-scale social media data were leveraged to interpret them from a doctrinal understanding of Chinese influence operations. Additionally, the authors used statistical methods to examine the relationship between China's information and military operations toward Taiwan. This paper finds that China uses its state-sponsored accounts to coordinate and amplify social media messaging around military campaigns with strategic importance. Additionally, the presence of a multipronged approach using social media to support military campaigns may indicate an escalation in conflict. [ABSTRACT FROM AUTHOR]

Although influenza vaccines are safe and efficacious, vaccination rates have remained low globally. Today, with the advent of new media, many individuals turn to social media for personal health questions and information. However, misinformation may be rife, and health communications may be suboptimal. This study, therefore, aimed to investigate the public messaging related to influenza vaccines by organizations over Twitter, which may have a far-reaching influence. The theoretical framework of the COM-B (capacity, opportunity, and motivation component of behavior) model was used to interpret the findings to aid the design of messaging strategies. Employing search terms such as "flu jab", "flu vaccine", "influenza vaccine", and '" influenza jab", tweets posted in English and by organizations from 1 January 2017 to 1 March 2023 were extracted and analyzed. Using topic modeling, a total of 235,261 tweets by organizations over Twitter were grouped into four main topics: publicizing campaigns to encourage influenza vaccination, public education on the safety of influenza vaccine during pregnancy, public education on the appropriate age to receive influenza vaccine, and public education on the importance of influenza vaccine during pregnancy. Although there were no glaring pieces of misinformation or misconceptions, the current public messaging covered a rather limited scope. Further information could be provided about influenza and the benefits of vaccination (capability), promoting community, pharmacist-led influenza vaccination, and other avenues (opportunity), and providing greater incentivization and support for vaccination (motivation). [ABSTRACT FROM AUTHOR]

Food waste is a complex issue requiring novel approaches to understand and identify areas that could be leveraged for food waste reduction. Data science techniques such as sentiment analysis, emotion analysis, and topic modelling could be used to explore big-picture themes of food waste discussions. This paper aimed to examine food waste discussions on Twitter and identify priority areas for future food waste communication campaigns and interventions. Australian tweets containing food-waste-related search terms were extracted from the Twitter Application Programming Interface from 2019–2021 and analysed using sentiment and emotion engines. Topic modelling was conducted using Latent Dirichlet Allocation. Engagement was calculated as the sum of likes, retweets, replies, and quotes. There were 39,449 tweets collected over three years. Tweets were mostly negative in sentiment and angry in emotion. The topic model identified 13 key topics such as eating to save food waste, morals, economics, and packaging. Engagement was higher for tweets with polarising sentiments and negative emotions. Overall, our interdisciplinary analysis highlighted the negative discourse surrounding food waste discussions and identified priority areas for food waste communication. Data science techniques should be used in the future to monitor public perceptions and understand priority areas for food waste reduction. [ABSTRACT FROM AUTHOR]

Increasing public concerns about the environment have led to many studies that have explored current issues and approaches towards its protection. Much less studied, however, is topic of public opinion surrounding the impact that cryptocurrencies are having on the environment. The cryptocurrency market, in particular, bitcoin, currently rivals other top well-known assets such as precious metals and exchanged traded funds in market value, and its growing. This work examines public opinion expressed about the environmental impacts of bitcoin derived from Twitter feeds. Three primary research questions were addressed in this work related to topics of public interest, their location, and people and places involved. Our findings show that factions of of the public are interest in protecting the environment, with topics that resonate mainly related to energy. This discourse was also taking place at few similar locations with a mix of different people and places of interest. [ABSTRACT FROM AUTHOR]

Since the outbreak of the COVID-19 pandemic, increases in suicidal ideation and suicide attempts in adolescents have been registered. Many adolescents experiencing suicidal ideation turn to online communities for social support. In this retrospective observational study, we investigated the communication—language style, contents and user activity—in 7975 unique posts and 51,119 comments by N = 2862 active adolescent users in a large suicidal ideation support community (SISC) on the social media website reddit.com in the onset period of the COVID-19 pandemic. We found significant relative changes in language style markers for hopelessness such as negative emotion words (+ 10.00%) and positive emotion words (− 3.45%) as well as for social disengagement such as social references (− 8.63%) and 2nd person pronouns (− 33.97%) since the outbreak of the pandemic. Using topic modeling with Latent Dirichlet Allocation (LDA), we identified significant changes in content for the topics Hopelessness (+ 23.98%), Suicide Methods (+ 17.11%), Social Support (− 14.91%), and Reaching Out to users (− 28.97%). Changes in user activity point to an increased expression of mental health issues and decreased engagement with other users. The results indicate a potential shift in communication patterns with more adolescent users expressing their suicidal ideation rather than relating with or supporting other users during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic disrupted societies all over the world. In an interconnected and digital global society, social media was the platform not only to convey information and recommendations but also to discuss the pandemic and its consequences. Focusing on the phase of stabilization during the first wave of the pandemic in Western countries, this work analyses the conversation around it through tweets in English. For that purpose, the authors have studied who the most active and influential accounts were, identified the most frequent words in the sample, conducted topic modelling, and researched the predominant sentiments. It was observed that the conversation followed two main lines: a more political and controversial one, which can be exemplified by the relevant presence of former US President Donald Trump, and a more informational one, mostly concerning recommendations to fight the virus, represented by the World Health Organization. In general, sentiments were predominantly neutral due to the abundance of information. [ABSTRACT FROM AUTHOR]

Interests play an essential role in the process of learning, thereby enriching learners 'interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset. [ABSTRACT FROM AUTHOR]

Increasing average temperatures and heat waves are having devasting impacts on human health and well-being but studies of heat impacts and how people adapt are rare and often confined to specific locations. In this study, we explore how analysis of conversations on social media can be used to understand how people feel about heat waves and how they respond. We collected global Twitter data over four months (from January to April 2022) using predefined hashtags about heat waves. Topic modelling identified five topics. The largest (one-third of all tweets) was related to sports events. The remaining two-thirds could be allocated to four topics connected to communication about climate-related heat or heat waves. Two of these were on the impacts of heat and heat waves (health impacts 20%; social impacts 16%), one was on extreme weather and climate change attribution (17%) and the last one was on perceptions and warning (13%). The number of tweets in each week corresponded well with major heat wave occurrences in Argentina, Australia, the USA and South Asia (India and Pakistan), indicating that people posting tweets were aware of the threat from heat and its impacts on the society. Among the words frequently used within the topic 'Social impacts' were 'air-conditioning' and 'electricity', suggesting links between coping strategies and financial pressure. Apart from analysing the content of tweets, new insights were also obtained from analysing how people engaged with Twitter tweets about heat or heat waves. We found that tweets posted early, and which were then shared by other influential Twitter users, were among the most popular. Finally, we found that the most popular tweets belonged to individual scientists or respected news outlets, with no evidence that misinformation about climate change-related heat is widespread. [ABSTRACT FROM AUTHOR]

Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR–SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms. [ABSTRACT FROM AUTHOR]

The scientist role has progressively gained an essential relevance during the 2020 pandemic. In fact, the virologists' exposition turned out to be fundamental for the public opinion, both for the well-informed and people unaware about health, transmission, infection and, today, vaccination programs. This paper aims to first set an explorative investigation about the social communication practices during the first three months of the vaccine campaign addressed on social media by Italy's most established virologists. The arising digital scenario and the resultant pervasive presence in our daily life of web platforms, such as social media, has revolutionized the nexus between science and society. More scholars argued about the disintermediated current shape of science communication that directly connects scientists and the larger public, driving the sociological debate towards the analysis of the current processes of sense-making construction. On this assumption, we aim to answer the research question about how Italian scientists communicate and approach the larger public on social media. Therefore, the empirical part of this paper consists of a data collection phase conducted on Facebook and Twitter. The collected data have been analyzed by a content analysis oriented to identify the contradictory or uniformity of disintermediated communication features of the observed social media profiles in order to push and follow, during the ongoing vaccine dosing program, a proactive reflection about the key role of scientific dissemination of information. [ABSTRACT FROM AUTHOR]

Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large textbased corpora gathered from social media is proposed. We explain the use of computermediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weightbased data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns. [ABSTRACT FROM AUTHOR]

Public opinion evaluation is becoming increasingly significant in infrastructure project assessment. The inefficiencies of conventional evaluation approaches can be improved with social media analysis. Posts about infrastructure projects on social media provide a large amount of data for assessing public opinion. This study proposed a hybrid model which combines pre-trained RoBERTa and gated recurrent units for sentiment analysis. We selected the United Kingdom railway project, High Speed 2 (HS2), as the case study. The sentiment analysis showed the proposed hybrid model has good performance in classifying social media sentiment. Furthermore, the study applies latent Dirichlet allocation topic modelling to identify key themes within the tweet corpus, providing deeper insights into the prominent topics surrounding the HS2 project. The findings from this case study serve as the basis for a comprehensive public opinion evaluation framework driven by social media data. This framework offers policymakers a valuable tool to effectively assess and analyse public sentiment. [ABSTRACT FROM AUTHOR]

This study aimed to study the public's sentiments on the current monkeypox outbreaks via an unsupervised machine learning analysis of social media posts. This was an exploratory analysis of tweets sentiments. We extracted original tweets containing the terms 'monkeypox', 'monkey pox' or 'monkey_pox' and posted them in the English language from 6 May 2022 (first case detected in the United Kingdom) to 23 July 2022 (when World Health Organization declared Monkeypox to be a global health emergency). Retweets and duplicate tweets were excluded from study. Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition. This was followed by topic modelling (specifically BERTopic) and manual thematic analysis by the study team, with independent reviews of the topic labels and themes. Based on topic modelling and thematic analysis of a total of 352,182 Twitter posts, we derived five topics clustered into three major themes related to the public discourse on the ongoing outbreaks. These include concerns of safety, stigmatisation of minority communities, and a general lack of faith in public institutions. The public sentiments underscore growing (and existing) partisanship, personal health worries in relation to the evolving situation, as well as concerns of the media's portrayal of lesbian, gay, bisexual, transgender and queer and minority communities, which might further stigmatise these groups. Monkeypox is an emerging infectious disease of public concern. Our study has highlighted important societal issues, including misinformation, political mistrust and anti-gay stigma that should be sensitively considered when designing public health policies to contain the ongoing outbreaks. [ABSTRACT FROM AUTHOR]

Objective: Compassion is important to patients and their families, predicts positive patient and practitioner outcomes, and is a professional requirement of physicians around the globe. Yet, despite the value placed on compassion, the empirical study of compassion remains in its infancy and little is known regarding what compassion 'looks like' to patients. The current study addresses limitations in prior work by asking patients what physicians do that helps them feel cared for. Methods: Topic modelling analysis was employed to identify empirical commonalities in the text responses of 767 patients describing physician behaviours that led to their feeling cared for. Results: Descriptively, seven meaningful groupings of physician actions experienced as compassion emerged: listening and paying attention (71% of responses), following‐up and running tests (11%), continuity and holistic care (8%), respecting preferences (4%), genuine understanding (2%), body language and empathy (2%) and counselling and advocacy (1%). Conclusion: These findings supplement prior work by identifying concrete actions that are experienced as caring by patients. These early data may provide clinicians with useful information to enhance their ability to customize care, strengthen patient–physician relationships and, ultimately, practice medicine in a way that is experienced as compassionate by patients. Public Contribution: This study involves the analysis of data provided by a diverse sample of patients from the general community population of New Zealand. [ABSTRACT FROM AUTHOR]