TY - JOUR AU - Hoştut, S. AU - Güdekli, İ.A. AU - Güzeldağ, F. TI - Safeguarding Truth in Turmoil: A Study of the Turkish Government’s Strategic Deployment of Twitter during the February 6, 2023, Earthquakes ST - Zor Zamanlarda Hakikati Koruma: 6 Şubat 2023 Depremleri Sırasında Türkiye Cumhuriyeti Hükümetinin Stratejik Twitter Kullanımı PY - 2024 T2 - Bilig VL - 2024 IS - 108 SP - 51 EP - 82 DO - 10.12995/bilig.10803 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184389139&doi=10.12995%2fbilig.10803&partnerID=40&md5=7c44a88bdfbce99ac969b28f15ec142a AB - This article examines crisis communication and public diplomacy through social media, especially in the context of the earthquakes that occurred in Türkiye on February 6, 2023. The study under-scores the critical roles of accurate information dissemination, public trust cultivation, and disinformation prevention. Focusing on the experiences of leading government offices, particularly their Twitter responses, this research demonstrates the interconne-ctedness of crisis communication, digital diplomacy and central importance of strategic integration in the digital age. Using a dataset of 2,997 tweets from six government Twitter accounts, the MAXQDA 2020 analysis explores the dynamics of the relationship between Turkish public diplomacy, crisis communication, and social media, and offers insights into resilient communication frameworks in global governance. The effectiveness of the government’s communication efforts is evident in the strategic use of Twitter as a dynamic tool for real-time dissemination of information during crises, underlining the government’s proactive and responsive stance in crisis communication. © 2024, Ahmet Yesevi University. All rights reserved. KW - Crisis communication KW - disinformation KW - earthquake KW - public diplomacy KW - social media KW - Türkiye M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Ng, L.H.X. AU - Carley, K.M. TI - Assembling a multi-platform ensemble social bot detector with applications to US 2020 elections PY - 2024 T2 - Social Network Analysis and Mining VL - 14 IS - 1 C7 - 45 DO - 10.1007/s13278-024-01211-2 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185884102&doi=10.1007%2fs13278-024-01211-2&partnerID=40&md5=1072315cc0b50d58f1940d2ffbab76c6 AB - Bots have been in the spotlight for many social media studies, for they have been observed to be participating in the manipulation of information and opinions on social media. These studies analyzed the activity and influence of bots in a variety of contexts: elections, protests, health communication and so forth. Prior to this analyzes is the identification of bot accounts to segregate the class of social media users. In this work, we propose an ensemble method for bot detection, designing a multi-platform bot detection architecture to handle several problems along the bot detection pipeline: incomplete data input, minimal feature engineering, optimized classifiers for each data field, and also eliminate the need for a threshold value for classification determination. With these design decisions, we generalize our bot detection framework across Twitter, Reddit and Instagram. We also perform feature importance analysis, observing that the entropy of names and number of interactions (retweets/shares) are important factors in bot determination. Finally, we apply our multi-platform bot detector to the US 2020 presidential elections to identify and analyze bot activity across multiple social media platforms, showcasing the difference in online discourse of bots from different platforms. © The Author(s) 2024. KW - Bot detection KW - Instagram KW - Interpretability KW - Machine learning KW - Reddit KW - Social media KW - Twitter KW - US 2020 elections KW - Botnet KW - Classification (of information) KW - Social networking (online) KW - Bot detections KW - Instagram KW - Interpretability KW - Machine-learning KW - Multi-platform KW - Reddit KW - Social bots KW - Social media KW - Twitter KW - US 2020 election KW - Machine learning M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Ma, Y. AU - Wu, P. AU - Ling, C. AU - Ding, S. TI - Research on public opinion effecting on stock price during crises based on model checking PY - 2024 T2 - Expert Systems with Applications VL - 249 C7 - 123442 DO - 10.1016/j.eswa.2024.123442 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185536366&doi=10.1016%2fj.eswa.2024.123442&partnerID=40&md5=2a79f719de7114ae17c5ddb92520db0f AB - Recent studies have shown that news and investors’ comments on social media against listed companies significantly impact stock price movements. Listed companies suffer abnormal stock price movements and tremendous economic losses in public opinion crises. Most existing studies combine news and investors’ sentiments with technical indicators to predict stock price movements to help investors optimize investments. However, it is urgent for listed companies to predict stock price movements during crises and understand the impact of multiple factors on stock price movements during crises, thereby using effective response strategies to stabilize stock prices. This study proposes a method to predict stock price movements during crises based on model checking. We integrate the public opinion factors by considering the interaction among investors, media and listed companies as crisis managers based on the Situational Crisis Communication Theory. The explicable rules of stock price movements are extracted by random forest algorithm from objective data of previous crises and formalized as Computation Tree Logic formulas (φ). The database is modeled into a verifiable formal model (M) and formalized as a Kripke structure. The model checker NuSMV is used to verify the rules in actual situations to predict stock price movements and provide early warning automatically. The proposed method achieves superior performance with ACC of 77.53% and 78.43% in the victim and preventable crises, respectively. The response strategies of listed companies significantly impact stock price movements during crises in the Chinese stock market. The explicable rules of stock price movements provide decision support to develop proper crisis responses for listed companies during crises. © 2024 Elsevier Ltd KW - Crisis response KW - Model checking KW - Public opinion KW - Stock price movements KW - Decision support systems KW - Financial markets KW - Forecasting KW - Information theory KW - Investments KW - Losses KW - Social aspects KW - Crisis response KW - Economic loss KW - Investor's sentiments KW - Models checking KW - Public opinion crisis KW - Public opinions KW - Response strategies KW - Social media KW - Stock price KW - Stock price movements KW - Model checking M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Sixto-García, J. AU - García-Orosa, B. AU - González-Lois, E. AU - Pascual-Presa, N. TI - Transparency on YouTube for radon risk communication ST - Transparencia en YouTube para la comunicación del riesgo del radón PY - 2025 T2 - Revista Latina de Comunicacion Social VL - 2025 IS - 83 SP - 1 EP - 20 DO - 10.4185/rlcs-2025-2266 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184733864&doi=10.4185%2frlcs-2025-2266&partnerID=40&md5=66074f5350c75a400b42bf73b03dc57f AB - Introduction: Scientific evidence has proven the link between exposure to radon in indoor environments and lung cancer. For this reason, radon gas is considered a threat to public health. Additionally, YouTube has also been confirmed as a source of medical information. Methodology: This research examines YouTube as a vehicle for the global dissemination of information about radon. All the channels available on the platform since its creation that contain videos on this gas are identified, along with the geographical areas in which they operate, the language they use to broadcast, the number of subscribers they have, and the number of views they amass. Using a sample of channels specifically focused on radon, the presence of this topic on YouTube is examined using a mixed methodological model (quantitative and qualitative) that explores themes, narratives and dissemination strategies. Results: The results reveal the absence of echo chambers and the lack of awareness on this social network regarding the public health issues surrounding radon gas. Discussion and Conclusions: The study highlights the limited presence of radon-related videos on YouTube, with a predominance of content in English, restricting accessibility in non-English-speaking regions. Radon channels underutilize YouTube features and lack community engagement, revealing a significant gap in recognizing radon as a public health issue on the platform. Successful channels demonstrate good practices, but overall awareness remains insufficient. © 2025, HISIN (History of Information Systems). All rights reserved. KW - Echo chambers KW - Public health KW - Radon KW - Risk KW - Risk communication KW - Social media KW - YouTube M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Alturayeif, N. AU - Luqman, H. AU - Ahmed, M. TI - Enhancing stance detection through sequential weighted multi-task learning PY - 2024 T2 - Social Network Analysis and Mining VL - 14 IS - 1 C7 - 7 DO - 10.1007/s13278-023-01169-7 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179122601&doi=10.1007%2fs13278-023-01169-7&partnerID=40&md5=499f0455bc316fed44d316c1205205a0 AB - The exponential growth of user-generated content on social media platforms, online news outlets, and digital communication has necessitated the development of automated tools for analyzing opinions and attitudes expressed in text. Stance detection, a critical task in Natural Language Processing, aims to identify the underlying perspective or viewpoint of an individual or group toward a specific topic or target. This paper explores the challenges of stance detection, particularly in the context of social media, where brevity, informality, and limited contextual information prevail. While sentiment analysis focuses on explicit sentiment polarity, stance detection classifies the stance or viewpoint of a text toward a target, often of an abstract nature. Motivated by recent achievements in Multi-Task Learning (MTL), this paper addresses the identified gap in the field, advocating further exploration in developing a joint neural architecture that integrates different opinion dimensions. In response, this study introduces two MTL models, Parallel Multi-Task Learning (PMTL) and Sequential Multi-Task Learning (SMTL), which incorporate sentiment analysis and sarcasm detection tasks to enhance stance detection performance. We address the complexities of MTL implementation with Transformer-based architectures and present an accessible architecture for this purpose. This study also proposes and evaluates four task weighting techniques, providing empirical evidence for their effectiveness in MTL models. Through comprehensive evaluations on benchmark datasets in both English and Arabic, we demonstrate that our most proficient model, a multi-target sequential MTL model with hierarchical weighting (SMTL-HW), achieves state-of-the-art results. These contributions underscore the potential of MTL in enhancing stance detection and offer valuable insights into the interaction between sentiment, stance, and sarcasm in text analysis. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. KW - Multi-task learning (MTL) KW - Natural language processing (NLP) KW - Opinion mining KW - Sarcasm detection KW - Sentiment analysis KW - Social media KW - Stance detection KW - Abstracting KW - Architecture KW - Digital communication systems KW - Learning algorithms KW - Learning systems KW - Linearization KW - Social networking (online) KW - Language processing KW - Multi-task learning KW - Multitask learning KW - Natural language processing KW - Natural languages KW - Opinion mining KW - Sarcasm detection KW - Sentiment analysis KW - Social media KW - Stance detection KW - Sentiment analysis M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Nokkaew, M. AU - Nongpong, K. AU - Yeophantong, T. AU - Ploykitikoon, P. AU - Arjharn, W. AU - Siritaratiwat, A. AU - Narkglom, S. AU - Wongsinlatam, W. AU - Remsungnen, T. AU - Namvong, A. AU - Surawanitkun, C. TI - Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis PY - 2024 T2 - Social Network Analysis and Mining VL - 14 IS - 1 C7 - 15 DO - 10.1007/s13278-023-01168-8 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179934414&doi=10.1007%2fs13278-023-01168-8&partnerID=40&md5=227840072290d3249dc1584c6abcd798 AB - Sentiment analysis is becoming a very popular research technique. It can effectively identify hidden emotional trends in social networks to understand people’s opinions and feelings. This research therefore focuses on analyzing the sentiments of the public on the social media platform, YouTube, about the Thailand-China high-speed train project and the Laos-China Railway, a mega-project that is important to the country and a huge investment to develop transportation infrastructure. It affects both the economic and social dimensions of Thai people and is also an important route to connect the rail systems of ASEAN countries as part of the Belt and Road Initiative. We gathered public Thai reviews from YouTube using the Data Application Program Interface. This dataset was used to train six sentiment classifiers using machine learning and deep learning algorithms. The performance of all six models by means of precision, recall, F1-score and accuracy are compared to find the most suitable model architecture for sentiment classification. The results show that the transformer model with the WangchanBERTa language model yields best accuracy, 94.57%. We found that the use of a Thai language-specific model that was trained from a large variety of data sources plays a major role in the model performance and significantly increases the accuracy of sentiment prediction. The promising performance of this sentiment classification model also suggests that it can be used as a tool for government agencies to plan, make strategic decisions, and improve communication with the public for better understanding of their projects. Furthermore, the model can be integrated with any online platform to monitor people's sentiments on other public matters. Regular monitoring of public opinions could help the policy makers in designing public policies to address the citizens’ problems and concerns as well as planning development strategies for the country. © 2023, The Author(s). KW - Deep learning KW - Government KW - Machine learning KW - Public opinion KW - Sentiment analysis KW - Social media KW - Application programs KW - Classification (of information) KW - Deep learning KW - E-learning KW - Investments KW - Learning algorithms KW - Learning systems KW - Railroad cars KW - Railroad transportation KW - Railroads KW - Rails KW - Social aspects KW - Social networking (online) KW - Deep learning KW - Government KW - High speed trains KW - Machine-learning KW - Mega projects KW - Public opinions KW - Sentiment analysis KW - Social media KW - Thailand KW - YouTube KW - Sentiment analysis M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Imran, S. AU - Yasmeen, R. AU - Mansoor, M. TI - Development and validation of self-assessment instrument to measure the digital professionalism of healthcare professionals using social media PY - 2024 T2 - BMC Medical Education VL - 24 IS - 1 C7 - 243 DO - 10.1186/s12909-024-05142-6 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186934540&doi=10.1186%2fs12909-024-05142-6&partnerID=40&md5=5e9ea583f20a4a946bcc86821962eb38 AB - Background: The use of social media across the globe has risen incrementally. During the COVID-19 pandemic, these sites undeniably provided new avenues for professional networking but also led to a surge in cases of online misconduct. Professionalism instruments and scales do not assess the digital attitude and behaviour of healthcare professionals (HCPs). The purpose of this study was to identify the domains and items of digital professionalism related to social media use and to validate a self-assessment instrument to assess the digital professionalism of HCPs using social media. Methods: An instrument development multiphase mixed method study (exploratory sequential) was conducted in two phases: item development and qualitative content validation followed by validation of the instrument. Feedback was taken from 15 experts for qualitative content validation in phase 1. In phase 2, content validity was established through three rounds of modified Delphi. Validity evidence was collected for the content (content validity index), response process (cognitive interviews), internal structure (confirmatory factor analysis), and internal consistency (Cronbach’s alpha). Results: The 48-item preliminary instrument was reduced to a 28-item instrument with eight domains: self-anonymity, privacy settings, maintenance of boundaries and confidentiality, conflict of interest, accountability, respect for colleagues, and ethics. The content validity index of the scale was 0.91. The reliability and construct validity of the instrument was established by responses from 500 healthcare professionals from multiple hospitals. Confirmatory factor analysis showed a model with a goodness-of-fit index of 0.86, root mean square error of approximation of 0.06, and observed normed χ2 of 2.7. The internal consistency through Cronbach's alpha α was 0.96. Conclusion: The digital professionalism self-assessment instrument (DP-SAI) has an appropriate level of content and measures the construct reliably. It can be used by medical doctors, dental clinicians, nurses, physiotherapists, and clinical pharmacists to self-assess and reflect on their social media practices. This will help to address these issues to enhance the quality of online communication through various social media platforms. © The Author(s) 2024. KW - Digital professionalism KW - Healthcare professionals KW - Reliability KW - Social media KW - Validity KW - Delivery of Health Care KW - Humans KW - Pandemics KW - Physical Therapists KW - Professionalism KW - Reproducibility of Results KW - Self-Assessment KW - Social Media KW - health care delivery KW - human KW - pandemic KW - physiotherapist KW - professionalism KW - reproducibility KW - self evaluation KW - social media M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Capriotti, P. AU - Carretón-Ballester, C. AU - Losada-Díaz, J.-C. TI - Analysing the influence of Universities’ content strategy on the level of engagement on social media PY - 2024 T2 - Communication and Society VL - 37 IS - 1 SP - 41 EP - 60 DO - 10.15581/003.37.1.41-60 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183045365&doi=10.15581%2f003.37.1.41-60&partnerID=40&md5=586481a72c28297d8601f73d77dc2de2 AB - Social media have become a key tool in the institutional communication of universities to disseminate content and establish interaction and dialogue with their publics. Content strategy in social networks is a relevant aspect to inform their audiences about their daily activities and position universities in the digital sphere. This article studies the influence of the different types of content posted by universities on their social networks on the level of engagement of their publics. We conducted a content analysis of more than 90,000 posts by 70 universities in three regions (Europe, the United States and Latin America) on their institutional profiles on three social networks (Twitter, Facebook and LinkedIn). The results show that the level of engagement achieved by the universities’ posts is very low. Universities clearly prioritize institutional content over functional content, and organizational topics are the most published on social networks. Institutional content achieves a higher level of engagement than functional content, and posts on organizational topics have the best level of engagement. Our study might refute the hypothesis that “functional content will generate a higher level of engagement than institutional content on social networks.” Thus, it can be concluded that the combination of content on social networks suggests that universities mainly use social networks to develop an institutional positioning strategy on social media. © 2024 Communication & Society. KW - content strategy KW - digital communication KW - higher education KW - institutional communication KW - social media KW - University M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Machmud, M. AU - Fatimah, J.M. AU - Sultan, M.I. AU - Farid, M. TI - Social media as communication tools for anti-corruption campaign in Indonesia PY - 2024 T2 - International Journal of Data and Network Science VL - 8 IS - 1 SP - 357 EP - 368 DO - 10.5267/j.ijdns.2023.9.018 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175476453&doi=10.5267%2fj.ijdns.2023.9.018&partnerID=40&md5=c7402f38a9af6fdf3455111211a45032 AB - Social media has proven to be quite effective in raising awareness and anti-corruption movements in society. This research aimed to analyze the use of social media Twitter as a means of the Corruption Eradication Commission (KPK) in conducting anti-corruption campaigns in Indonesia. The research employed a qualitative content analysis on the KPK's official Twitter account. The data were processed using the NVIVO 12 Plus software to answer research questions. This research revealed that the KPK's Twitter account is quite active in carrying out anti-corruption campaign activities, although in general it is not optimal. It can be seen from the low intensity of communication and limited communication network so that it is considered as less collaborative. Improving the problems is needed by KPK as it must also show good performance so that public trust continues in high condition. However, this research has limitations in looking at all anti-corruption campaigns carried out by the KPK because it only used Twitter as the reference. There-fore, further research is suggested to analyze all KPK social media such as Youtube and Instagram. © 2024 by the authors. KW - Anti-corruption campaigns KW - Collaborative KW - Communication KW - Network KW - Public trust KW - Social media M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER - TY - JOUR AU - Chandrasekaran, R. AU - Konaraddi, K. AU - Sharma, S.S. AU - Moustakas, E. TI - Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube PY - 2024 T2 - Journal of Medical Systems VL - 48 IS - 1 C7 - 21 DO - 10.1007/s10916-024-02047-1 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185322595&doi=10.1007%2fs10916-024-02047-1&partnerID=40&md5=33f251097275eddc16139282d33a1dcb AB - This study explores how individuals who have experienced COVID-19 share their stories on YouTube, focusing on the nature of information disclosure, public engagement, and emotional impact pertaining to consumer health. Using a dataset of 186 YouTube videos, we used text mining and video analytics techniques to analyze textual transcripts and visual frames to identify themes, emotions, and their relationship with viewer engagement metrics. Findings reveal eight key themes: infection origins, symptoms, treatment, mental well-being, isolation, prevention, government directives, and vaccination. While viewers engaged most with videos about infection origins, treatment, and vaccination, fear and sadness in the text consistently drove views, likes, and comments. Visuals primarily conveyed happiness and sadness, but their influence on engagement varied. This research highlights the crucial role YouTube plays in disseminating COVID-19 patient narratives and suggests its potential for improving health communication strategies. By understanding how emotions and content influence viewer engagement, healthcare professionals and public health officials can tailor their messaging to better connect with the public and address pandemic-related anxieties. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. KW - COVID-19 Discourse KW - Emotion Analysis KW - Topic Modeling KW - Video Analytics KW - YouTube KW - Anxiety KW - COVID-19 KW - Data Mining KW - Emotions KW - Humans KW - Social Media KW - anxiety KW - Article KW - content analysis KW - controlled study KW - coronavirus disease 2019 KW - data mining KW - emotion KW - government KW - happiness KW - human KW - infection prevention KW - information dissemination KW - medical information KW - narrative KW - pandemic KW - personal experience KW - psychological well-being KW - sadness KW - social isolation KW - social media KW - vaccination KW - videorecording KW - adult KW - article KW - benchmarking KW - emotion KW - fear KW - health care personnel KW - interpersonal communication KW - prevention M3 - Article DB - Scopus N1 - Export Date: 05 April 2024; Cited By: 0 ER -