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The authors examine the effects of a firm's and its competitors' online reviews on its demand within the hotel industry. The authors leverage a unique data set of actual bookings from properties of a major hotel chain in six different markets in the United States, supplemented with online reviews garnered from a popular social media platform. The findings indicate that not only a hotel's own reviews but also its competitors' reviews have a significant impact on the hotel's booking performance. The impact of review sentiment is amplified if the focal hotel also charges higher prices or when the volume of reviews is high. The authors establish heterogeneous effects across consumer segments (business vs. leisure travelers) and by the type of review content (objective vs. subjective attributes to assess quality). Specifically, both a hotel's own reviews and its competitors' reviews have a larger impact on bookings for business travelers compared with leisure travelers, and for reviews that mainly discuss subjective attributes, for which consumers need to rely on the experiences of others to assess the quality of a hotel prior to their stay. The study provides a set of comprehensive insights on the impact of both own and competitors' online reviews on a focal hotel's bookings. [ABSTRACT FROM AUTHOR]

This paper introduces the "Intelligent Analysis System for Arabic Tweets" (IASAT), an innovative framework designed to tackle the specific challenges of sentiment analysis in the Arabic language, particularly in educational contexts. IASAT was developed to analyze student sentiments from social media platforms like Twitter, addressing the unique complexities of Arabic, including its rich morphology, diverse dialects, and syntactic variations. The system employs advanced machine learning algorithms, allowing for more precise sentiment classification tailored to the intricacies of Arabic, making it particularly useful for understanding feedback in real time. IASAT achieved significant improvements over traditional methods, with accuracy rates averaging 82.4%, and reaching as high as 93% in some cases. Moreover, the system demonstrated a 12.8% reduction in computational latency, ensuring both efficiency and scalability for large-scale sentiment analysis tasks. These results highlight IASAT's potential as a powerful tool for educational institutions to analyze student feedback, offering actionable insights that can be used to improve educational services. IASAT represents a practical and scalable solution for the challenges of Arabic sentiment analysis, enabling more accurate and efficient understanding of student opinions on social. [ABSTRACT FROM AUTHOR]

Introduction: Understanding consent is essential to combat sexual violence, a deeply rooted social problem. Amidst its complexities, the scientific literature has emphasized the shortcomings of only considering the speech act—whether the victim-survivor said "yes" or not. Instead, sociological research underscores the need to analyze the whole communicative act where different elements lead to either a power relationship where there is no consent or a dialogic relationship where freedom is granted. Although some research has been conducted on citizens' social media debates on consent, how such debates include the concept of communicative acts to discuss it has not been analyzed yet Methods: 55 gender-related Instagram and Twitter (now known as X) posts—published and extracted over the course of 14 days—were analyzed. Results: Findings reveal that most posts refer to Power Communicative Acts as a hindrance for consent due to hierarchical power imbalances or to coercion, and called for the need to establish elements of Dialogic Communicative Acts to achieve consent and construct more egalitarian environments. Finally, most posts that considered ethics spoke about the need for perpetrators to be held accountable or offered similar takes on consequentialism. Discussion: These findings help illustrate how several social media debates about consent successfully fall into the Communicative Acts framework. [ABSTRACT FROM AUTHOR]

The rapid rise of social media and widespread Internet access have contributed significantly to the global popularity of e-sports. However, while popular e-sports attract considerable attention, niche e-sports remain underexplored, limiting user discovery and engagement. This paper proposes a Twitter-based recommendation system that uses advanced data management and processing techniques to address the challenge of identifying and recommending both popular and niche e-sports. The system analyzes social media metadata, including user IDs, followers, followees, engagements, and impressions, to calculate two critical metrics: popularity and satisfaction. Based on the combination of these metrics, the system calculates overall scores for each e-sports and generates two distinct rankings: one for popular and another for niche e-sports. The proposed system reflects the application of data-driven methodologies and social network analysis in creating recommendations that meet diverse user preferences, highlighting the relevance of data processing technologies in personalized content delivery. Experimental evaluations, using a dataset derived from Twitter hashtags (#) representing 30 target e-sports in 2022, demonstrate the system's effectiveness in capturing the emerging dynamics in e-sports and providing actionable insights for diverse user preferences. This study highlights the potential of SNS-based technologies to advance data processing, analysis, and application within the e-sports ecosystem. [ABSTRACT FROM AUTHOR]

This study explores intellectual capital's impact on social media analytics adoption in the banking industry. It also examines the impact of social media analytics on competitive intelligence and banking entrepreneurship. Furthermore, this study explores the mediating role of competitive intelligence in the impact of social media analytics on banking entrepreneurship. Data were collected from 320 bank managers and specialists and analyzed using Smart PLS, Version 4. The findings indicated that human, structural, and relational capital significantly impact the adoption of social media analytics. This study also revealed that social media analytics significantly impacts competitive intelligence and banking entrepreneurship. Furthermore, the results showed a significant mediating role of competitive intelligence in the impact of social media analytics on banking entrepreneurship. This study provides invaluable contributions for both academic discourse and industry professionals. It thoroughly investigates the interrelationships among intellectual capital, social media analytics, competitive intelligence, and banking entrepreneurship. This study advances our understanding of how these capabilities operate within the ever‐changing banking realm. Moreover, this study provides novel practical insights for bank managers and policymakers, emphasizing the importance of intellectual capital as well as the contributions of social media analytics and competitive intelligence in driving banking entrepreneurship. [ABSTRACT FROM AUTHOR]

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Suicide is a global public health concern, with the World Health Organization (WHO) identifying it as the second leading cause of death among individuals aged from 15 to 29 years. In Malaysia, recent statistics indicate a 10% increase in suicide cases in 2023 compared to the previous year. Online forums, such as Reddit, have become platforms for sharing opinions on this matter. Extracting and automatically analyzing these discussions can provide valuable insights into public opinion concerning this issue. While the existing research primarily focuses on identifying suicidal ideation from posts, the work on discerning public opinion remains limited. This study scraped opinion posts on suicide from the Malaysian Reddit community. Sentiment Analysis (SA) was conducted using a lexicon-based sentiment analyzer and topic modeling was performed by deploying Latent Dirichlet Allocation (LDA). The analysis revealed the following insights: (1) predominantly negative sentiments were detected in the opinions, both overall and within identified topics and (2) topic modeling indicated two distinctive topics reflecting the different perspectives and concerns of the Muslim and non-Muslim communities. Specifically, the overall SA revealed that 58% of the posts were negative, 10% were neutral, and 32% were positive. Within the identified topics, the Muslim community expressed a notably higher percentage of negative sentiments at 64%, compared to the 50% found in the non-Muslim community. These findings offer evidence-based insights into public opinion regarding suicide in Malaysia, contributing to the understanding of societal perspectives on this critical issue. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications. [ABSTRACT FROM AUTHOR]

This study investigates the complex urban-rural dynamics of Nanjing through a novel multi-modal analysis of 76,288 social media posts, addressing the critical gap in understanding intra-city variations in perceived urban characteristics across rapidly developing Chinese cities. By integrating computer vision techniques with natural language processing, we develop a comprehensive framework for analyzing public sentiment and attention patterns across eleven districts. Our findings reveal distinct spatial gradients in urban perception that challenge traditional urban-rural dichotomies. While central districts exhibit higher positive sentiments toward built environment (0.65-0.71) and economic factors, peripheral areas show stronger positive associations with environmental quality (0.42) and community cohesion (0.47). Correlation analysis demonstrates significant relationships between socioeconomic indicators and digital engagement patterns, with education levels strongly correlating with cultural heritage attention (r=0.76) and income levels with economic discourse (r=0.94). SHAP analysis further reveals non-linear interaction effects between urban characteristics and public sentiment, particularly in transitional zones. These findings contribute to theories of post-reform Chinese urbanization while offering practical insights for targeted urban planning strategies. The study’s methodological framework provides a replicable approach for analyzing intra-city variations in urban perceptions across rapidly urbanizing regions.

This study examines laptop user sentiments across developed and developing countries using aspect-based sentiment analysis (ABSA) on social media data. Employing a mixed-methods approach, the research combines large-scale sentiment analysis of 1,467,535 tweets with expert insights gathered through a Delphi panel of ten industry professionals. The study analyzes user opinions on 16 key laptop features, revealing distinct preferences and pain points between users in developed and developing nations. Findings show that consumers in developing countries express higher overall satisfaction, while those in developed countries demonstrate more critical views, particularly regarding performance-related features. Universal concerns include battery life and cooling efficiency. The research identifies key priorities for each market: high performance and advanced features for developed countries, and affordability, durability, and reliability for developing nations. Based on these insights, the study proposes tailored strategies for laptop manufacturers, including differentiated product lines, universal charging solutions, and market-specific innovations in cooling systems and warranties. The methodology incorporates data preprocessing, ABSA implementation using the PyABSA framework, and a three-phase Delphi process for expert analysis. This comprehensive analysis provides valuable guidance for product development and marketing strategies in the global laptop industry, emphasizing the importance of market-specific approaches to enhance user satisfaction and maintain competitiveness. The study also acknowledges limitations and suggests directions for future research, including multi-source data analysis and longitudinal studies.

Sosyal medya, geleneksel ağızdan ağıza iletişimi dijital platformlara taşıyarak bireyler ve kurumların iletişim biçimlerinde büyük değişikliklere yol açmıştır. Sosyal medya kullanıcılarının etkileşimlerinden oluşan büyük veri kümeleri, araştırmacılar için önemli birer kaynak haline gelmiştir. Bu verilerin analizine yönelik çalışmalar hızla artmakta ve sosyal medya analitiği verilerinin bilimsel araştırmalara katkısı giderek önem kazanmaktadır. Bibliyometrik çalışmalar da bu bağlamda akademik trendleri izlemek için kritik bir araç olarak öne çıkmaktadır. Bu çalışmanın amacı, "Sosyal Medya Analitiği" üzerine yayımlanan uluslararası araştırmaların bibliyometrik yöntemle incelenmesi ve bu yayınlar arasındaki ilişkilerin somut bir şekilde ortaya konulmasıdır. 24 Temmuz 2024'te Scopus veri tabanında yapılan bir arama sonucunda, sosyal medya analitiğiyle ilgili 6.541 esere ulaşılmıştır. Bu eserler, yıl, ülke, yazar, atıf ve anahtar kelime kullanımı gibi kriterlere göre analiz edilmiştir. Çalışmada sadece bibliyometrik özelliklerin görselleştirilmesi değil, aynı zamanda bu yayınlar arasındaki ilişkilerin derinlemesine anlaşılmasına da olanak sağlanmıştır. Verilerin kullanıldığı bağlamların analizi, literatürdeki bilgi boşluklarını belirlemekte ve gelecekteki çalışmalara yol göstermektedir. Çalışma bulgularına göre sosyal medya analitiği konusunda yılının ilk ve tek yayını 2005 yılında görülmüş olup 2009 yılından itibaren çalışmaların sayısı artan bir ivmeyle çoğalmıştır. Ayrıca en çok çalışmanın yapıldığı ülke ABD, en çok iş birliği yapılan kurum Indian Institute of Technology Delhi, en çok atıf alan yazar Carlos Castillo, en çok araştırma yapılan anahtar kelimenin social media (sosyal medya) olduğu tespit edilmiştir. Yıllara göre araştırma trendinin büyük veri, duygu analizi, derin makine öğrenmesi, yapay zekâ konularına doğru yoğunlaştığı, küresel pandeminin yaşandığı dönemlerde pandemi özelinde araştırmaların yapıldığı sonucuna varılmıştır.

Online equity markets have significantly changed the dynamics of connecting angels and individual equity investors to new ventures that seek early-stage capital. However, for those early-stage investors, information pointing to the success of business-to-business (B2B) new ventures (B2BNVs) is scattered and disconnected. This paper focuses on social media narratives (SMNs) as a source of insight for such investors and proposes that predicting a B2BNV's likelihood of success requires a comparative view, i.e., a comparison of its SMNs with those of its competitors and customers. We expect that higher (lower) lingual similarity between the SMNs of an early-stage B2BNV and those of its prospective customers (competitors) predict its success. Using a longitudinal panel of 574 B2BNVs resulting in more than 2,700 venture-round observations, we find that a comparative view of a venture's SMNs can give early-stage investors reliable predictions about the B2BNV's ability to manage its market presence and its success in later stages. Our models show that a comparative view of SMNs increases the accuracy of predicting a B2BNV's later-stage fundraising success by an average of 15%. Furthermore, predictive models can reliably point to a successful market presence in later stages, including the landing of customers, the winning of awards and competitions, the receiving of endorsements, the generating of revenue, and the successful patenting of products. Our study contributes to existing literature that focuses on the business impacts of social media by demonstrating the usefulness of comparative linguistics in social media analytics, i.e., comparing the firm's social media communications to those of its competitors and business customers in the prediction of the entrepreneurial firm's success. [ABSTRACT FROM AUTHOR]

The CoWIN Twitter Dataset offers a wide-ranging collection of public opinions on India's COVID-19 vaccination platform CoWIN. The raw dataset has 635,000 tweets that mention “cowin,” collected over the period of January to December 2021. The dataset was extracted by employing the Twitter Academic API. It addition to the raw data, it also included a cleaned and processed set of 419,409 English tweets, and a labeled subset with sentiment analysis. The raw data file has tweet details like ID, text, timestamp, user ID, and language. The processed dataset is devoid of URLs and hashtags and other noise, and also adds month and category groupings. Finally,the labelled dataset gives sentiment classifications of positive or negative the relevant tweets. This dataset enables researchers to analyse themes and sentiments related to India's vaccination administration. It can help policymakers gain insights around issues related to large-scale health initiatives and digital health systems. The mix of languages in the data also makes it useful for language processing research.

IntroductionUnderstanding consent is essential to combat sexual violence, a deeply rooted social problem. Amidst its complexities, the scientific literature has emphasized the shortcomings of only considering the speech act—whether the victim-survivor said “yes” or not. Instead, sociological research underscores the need to analyze the whole communicative act where different elements lead to either a power relationship where there is no consent or a dialogic relationship where freedom is granted. Although some research has been conducted on citizens’ social media debates on consent, how such debates include the concept of communicative acts to discuss it has not been analyzed yetMethods55 gender-related Instagram and Twitter (now known as X) posts—published and extracted over the course of 14 days—were analyzed.ResultsFindings reveal that most posts refer to Power Communicative Acts as a hindrance for consent due to hierarchical power imbalances or to coercion, and called for the need to establish elements of Dialogic Communicative Acts to achieve consent and construct more egalitarian environments. Finally, most posts that considered ethics spoke about the need for perpetrators to be held accountable or offered similar takes on consequentialism.DiscussionThese findings help illustrate how several social media debates about consent successfully fall into the Communicative Acts framework.

Social media has become an integral part of people's lives. Extant research is full of ample evidence on the impact of social media on the brand image and purchase intentions of consumers. Educational institutions have started using social media for passing information and influencing the attitudes of prospective students. There is a dearth of research on the type of content that can be used on social media to build the desired brand image as well as influence the attitude of prospective students. This study has an objective of identifying the common themes of social media content that are used by the leading IIMs, that is, IIM Ahmedabad, IIM Bangalore and IIM Calcutta. Additionally, another aim is to find the themes that drive maximum engagement. Data was extracted from Facebook and Instagram posts of IIM Ahmedabad, Bangalore and Calcutta for a period of 8 months (i.e., January 2022 to August 2022). The posts were categorized on the basis of commonality and were given a common theme for analysis. Simple descriptive statistics was used to analyse the data. Frequency and mean were used to identify the most popular themes and correlation was used to find the relationship between the number of posts and engagement. Text analysis was also done using word cloud. On the basis of the post content and most frequently used words on all three platforms, word cloud was built for the three institutions. It was found that the most engaging themes on Facebook were achievements, webinars, festival celebrations, public talks by experts and appreciation posts. Similarly, the most engaging themes on Instagram were university infrastructure, webinars, achievements, cultural events and festival celebrations, Furthermore, the most engaging themes on LinkedIn were farewell, inauguration, festival celebration, social events and virtual meetings. The results of this study will contribute to the extant literature on what branding themes can be used by educational institutions on social media platforms to maximize engagement. [ABSTRACT FROM AUTHOR]

The paper illustrates how social media marketing and analytics can assist businesses in achieving branding objectives by increasing their social media impact through advertising, getting social, using appropriate keywords, and creating effective and interactive communication channels with their intended audience. The multifaceted influence of social media postings is demonstrated by leveraging the Toyota Motor Corporation example. An explanation of how defamatory incidents have impacted the company’s social media atmospheric image is also included. Evidence of how negative intervals of social media presence could have been intelligently reversed through efficient content infusions is presented. Among numerous instruments allowing for analysis of marketing strategy results, “Social Mention”, “Talkwalker” and “Mentiolytics” were selected in order to demonstrate the efficiency and utility of social media monitoring methods using freely available tools. In this study, Toyota’s social media marketing strategy is highlighted by presenting measurements of variables such as the reach, strength, passion and sentiment of the brand over randomly selected time windows, demonstrating the dynamically evolving field of social media monitoring techniques.

Social media analytics is the backbone of successful marketing. It allows you to accurately find and understand the target audience of the company. However, this requires analyzing a huge amount of publicly available data. This is where software tools come to the rescue, allowing such analysis to be carried out in real time. Nevertheless, there are a large number of software products on the market, each of which has a different set of functions - from complex products to highly specialized ones. A wide or vice versa narrow range of functions makes it difficult to choose a specific software solution for a specific task. For this reason, the main purpose of the article is to make a comparative analysis of the functions of the most popular social media analytics software tools designed to identify and analyze the target audience.

Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications.

Medication nonadherence (MNA) can lead to serious health ramifications and costs U.S. healthcare systems $290 billion annually. Understanding the reasons underlying patients' MNA is thus an urgent goal for researchers, practitioners, and the pharmaceutical industry in order to mitigate negative health and economic consequences. In recent years, patient engagement on social media sites has soared, making it a cost-efficient and rich information source that can complement prior survey studies and deepen the understanding of MNA. Yet these data remain untapped in existing MNA studies because of technical challenges such as long texts, decision-making based on negative sentiment, varied patient vocabulary, and the scarcity of relevant information. For this study, we developed a sentiment-enriched deep learning method (SEDEL) to address these challenges and extract reasons for MNA. We evaluated SEDEL using 53,180 reviews concerning 180 drugs and achieved a precision of 89.25%, a recall of 88.48%, and an F1 score of 88.86%. SEDEL significantly outperformed state-of-the-art baseline models. We identified nine categories of MNA reasons, which were verified by domain experts. This study contributes to IS research by devising a novel deep-learning-based approach for reason mining and by providing direct implications for the health industry and for practitioners regarding the design of interventions. [ABSTRACT FROM AUTHOR]

The purpose of this research is to provide a local model of an accountant's career success. This research is applied in terms of the goal, and a descriptive-exploratory and mixed research in terms of data collection. The research method is conducted in four steps, the first step is carried out based on qualitative content analysis and reliability method in order to measure the validity. Achieving the initial conceptual structure of accountants' career success factors is the result obtained from this step. In the second step, the obtained influential factors are verified two steps based on the Fuzzy Delphi method by the experts. The statistical population in this step consists of 8 members of scientific practical experts including professional and trained auditors and accountants and university professors selected based on theoretical sampling, utilization of targeted (judgmental) and snowball techniques. Subsequently, in the third step, experts' subjective models are extracted and analyzed based on the fuzzy cognitive mapping method, and the type of relationship and the intensity of accountants' career success factors are verified by 22 experts. Next, several scenarios are simulated in order to improve the factors and their role in devising the model. The utilized software to implement fuzzy cognitive mapping is FCMAPPER. Moreover, in the fourth step, the dynamic model of accountants' career success factors is devised and extracted by using social media analytics and UCINET software. based on the analysis of the represented comments, the generality of the model consists of 21 factors of indication, modeling, and verification. the final model indicated career success as a function of important models such as work environment, knowledge, and emotional quotient (EQ) which is guided towards individual, social, and organizational outcomes with the involvement of financial factors and organizational climate, family, and university backgrounding with the support of operational solutions and techniques. [ABSTRACT FROM AUTHOR]

The rise of social media networks and their increasingly significant role in democratic life, epitomized by uprisings like the Arab Spring (Wolfsfeld et al., 2013), have revived hopes for democratization from below and the lively participation of the general population to produce dynamic system change. However, while social media allows for increased political awareness, it often inadvertently supports disseminating fake news and rumors that interfere with political reality. This article examines the most popular fake news stories, and rumors circulated during the 2018 presidential election in Cameroon through social media analytics. This research showcases the importance of homophily and echoes chambers in disseminating information on different political digital networks in Cameroon, revealing how fake news spread quickly during the campaign. Nevertheless, this research also shows that while specific fake news stories and rumors were not deemed plausible by the general public, they nevertheless shaped public perceptions and succeeded in maintaining the ruling party's political dominance, underscoring the profound impact of misinformation on public opinion and political control. [ABSTRACT FROM AUTHOR]

The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making. [ABSTRACT FROM AUTHOR]