Search Results (64)

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article

The field of text summarization has evolved from basic extractive methods that identify key sentences to sophisticated abstractive techniques that generate contextually meaningful summaries. In today’s digital landscape, where an immense volume of textual data is produced every day, the need for concise and coherent summaries is more crucial than ever. However, summarizing short texts, particularly from platforms like Twitter, presents unique challenges due to character constraints, informal language, and noise from elements such as hashtags, mentions, and URLs. To overcome these challenges, this paper introduces a deep learning framework for automated short text summarization on Twitter. The proposed approach combines bidirectional encoder representations from transformers (BERT) with a transformer-based encoder–decoder architecture (TEDA), incorporating an attention mechanism to improve contextual understanding. Additionally, long short-term memory (LSTM) networks are integrated within BERT to effectively capture long-range dependencies in tweets and their summaries. This hybrid model ensures that generated summaries remain informative, concise, and contextually relevant while minimizing redundancy. The performance of the proposed framework was assessed using three benchmark Twitter datasets—Hagupit, SHShoot, and Hyderabad Blast—with ROUGE scores serving as the evaluation metric. Experimental results demonstrate that the model surpasses existing approaches in accurately capturing key information from tweets. These findings underscore the framework’s effectiveness in automated short text summarization, offering a robust solution for efficiently processing and summarizing large-scale social media content. Full article

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. Full article

We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent’s characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent’s emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence. Full article

This article presents CapFlow, an integrated approach to detailed image captioning and hashtag generation. Based on a thorough performance evaluation, the image captioning model utilizes a fine-tuned vision-language model with Low-Rank Adaptation (LoRA), while the hashtag generation employs the keyword extraction method. We evaluated the state-of-the-art image captioning models using both traditional metrics (BLEU, METEOR, ROUGE-L, and CIDEr) and the specialized CAPTURE metric for detailed captions. The hashtag generation models were assessed using precision, recall, and F1-score. The proposed method demonstrates competitive results against larger models while maintaining efficiency suitable for real-time applications. The image captioning model outperforms the base Florence-2 model and favorably compares with larger models. The KeyBERT implementation for hashtag generation surpasses other keyword extraction methods in both accuracy and speed. This work contributes to the field of AI-assisted content analysis and generation, offering insights into the practical implementation of advanced vision-language models for detailed image understanding and relevant tag generation. Full article

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

Since orthopedic surgery has been slower to acknowledge the rise of social media for distributing medical information, this study aims to evaluate TikTok videos’ quality and educational value in relation to carpal tunnel syndrome treatment exercises. TikTok was searched using the hashtags “#carpaltunnelexercises”, “#carpaltunnelremedies”, “#carpaltunnelrehab”, and “#physicaltherapyforcarpaltunnel”. The engagement indicators were documented and the video content quality was assessed using the DISCERN, CTEES, JAMA, and GQS grading scales. There were 101 videos included, which accumulated 20,985,730 views. The videos received 1,460,953 likes, 15,723 comments, 243,245 favorites, and 159,923 shares. Healthcare professionals were responsible for 72% of the video uploads, whereas general users contributed 28%. More healthcare professionals’ videos were graded as “poor” (79%) compared to general users (21%). General users received slightly more video grades of “very poor” (52%) than healthcare professionals (48%). For the DISCERN grading, the videos by healthcare professionals were significantly better than those by general users in terms of reliability, achieving aims, and relevancy. They were also superior in the overall composition of the health information derived from the total DISCERN score. However, no significant differences were found between the two groups when using the CTEES, JAMA, and GQS grading scales. Overall, despite the emergence of TikTok as a medical information tool, the quality and educational value of the carpal tunnel syndrome exercise videos were poor. Full article

Television series enhance the social visibility of their content, as is the case with Queen Elizabeth II and The Crown. Netflix is the streaming television platform that has turned Peter Morgan’s successful series (2016) into a television icon where the monarch is the main protagonist, taking us on a biographical journey that mixes the historical and the political with fiction. The main character is made to seem more humane and is brought closer to the general public, all of which leads to a transmedia narrative. This research aims to analyze the content of the messages published on Twitter during the days surrounding the Queen’s State funeral in September 2022 and their connection with the series through the hashtag #TheCrown. The topics that have become trends worldwide are quantitatively analyzed, using different digital tools. The sample collected 1,489,279 tweets published during the days from the announcement of the death of Elizabeth II to the day of her funeral (from 8 to 19 September 2022). The results show nodes of connection between different players and linked communities to #TheCrown while offering the traffic generated by the hashtag with different nodes and edges. Full article

This study examines how U.S. senators strategically used hashtags to create political communities on Twitter during the 2022 Midterm Elections. We propose a way to model topic-based implicit interactions among Twitter users and introduce the concept of Building Political Hashtag Communities (BPHC). Using multiplex network analysis, we provide a comprehensive view of elites’ behavior. Through AI-driven topic modeling on real-world data, we observe that, at a general level, Democrats heavily rely on BPHC. Yet, when disaggregating the network across layers, this trend does not uniformly persist. Specifically, while Republicans engage more intensively in BPHC discussions related to immigration, Democrats heavily rely on BPHC in topics related to identity and women. However, only a select group of Democratic actors engage in BPHC for topics on labor and the environment—domains where Republicans scarcely, if at all, participate in BPHC efforts. This research contributes to the understanding of digital political communication, offering new insights into echo chamber dynamics and the role of politicians in polarization. Full article

Hashtag movements, also amongst contemporary student movements, present a new charge towards decolonization. This also happens at the Faculty of Theology of Stellenbosch University. The group also identified as a Black collective. This contribution argues that this charge is therefore at a deeper level, directed at older generations of Black theologians, and this is assessed critically through a reading of some proponents of third-generation South African Black theologians. It is concluded that there needs to be a conscious nurture of creative tension and challenge, transformative encounters to decolonize theological education in Africa. Full article

The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been published in the field of NLP. In this study, we created Kuwaiti dialect language resources using Q8VaxStance, a vaccine stance labeling system for a large dataset of tweets. This dataset fills this gap and provides a valuable resource for researchers studying vaccine hesitancy in Kuwait. Furthermore, it contributes to the Arabic natural language processing field by providing a dataset for developing and evaluating machine learning models for stance detection in the Kuwaiti dialect. The proposed vaccine stance labeling system combines the benefits of weak supervised learning and zero-shot learning; for this purpose, we implemented 52 experiments on 42,815 unlabeled tweets extracted between December 2020 and July 2022. The results of the experiments show that using keyword detection in conjunction with zero-shot model labeling functions is significantly better than using only keyword detection labeling functions or just zero-shot model labeling functions. Furthermore, for the total number of generated labels, the difference between using the Arabic language in both the labels and prompt or a mix of Arabic labels and an English prompt is statistically significant, indicating that it generates more labels than when using English in both the labels and prompt. The best accuracy achieved in our experiments in terms of the Macro-F1 values was found when using keyword and hashtag detection labeling functions in conjunction with zero-shot model labeling functions, specifically in experiments KHZSLF-EE4 and KHZSLF-EA1, with values of 0.83 and 0.83, respectively. Experiment KHZSLF-EE4 was able to label 42,270 tweets, while experiment KHZSLF-EA1 was able to label 42,764 tweets. Finally, the average value of annotation agreement between the generated labels and human labels ranges between 0.61 and 0.64, which is considered a good level of agreement. Full article

Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people’s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author’s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%. Full article

This study examines public policy advocacy by pediatricians and other health professionals in the hashtag community: #putkids1st. The study explores 4321 tweets that feature the hashtag, generated by 1231 unique users largely drawn from the American Association of Pediatricians and its members. The data are used to explore the structural dynamics of the hashtag community, the role of homophily, and to test a source-message framework to predict and recommendations to help improve engagement and retransmission of professional health advocacy messages. Full article

Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively. Full article

This article shows the use of Twitter that the main official spokespersons of the Spanish government made during the first weeks of the pandemic, with the aim of analyzing how government health campaigns were managed during the exceptional period of the state of alarm to deal with the COVID-19 pandemic and whether the instructions in terms of institutional management of communication to combat the infodemic set by the World Health Organization (WHO) were followed. This research considers the diffusion of official information in different phases of the first three months of the government’s action (102 days) from the outbreak of COVID-19 in Spain (March 2020) and how it developed its approach to crisis communication using the Twitter accounts of the President of the Spanish government (@sanchezcastejon), front-line leaders and the Ministry of Health (@sanidadgob), the main public institution responsible for health crisis management with the hashtags #EsteVirusLoParamosUnidos and #COVID-19. The results of a sample of 750 tweets reveal how the official sources used a model of online communication with a particular emphasis on informative and motivational tweets from leaders aimed at audiences (media and the general public). At the same time, there is also an instructive function about the pandemic towards audiences (general public and companies), with the Ministry and health authorities playing a key, proactive role in an attempt to achieve informative transparency to mitigate the pandemic and infodemic. Full article