Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Fuzzy-based machine learning for predicting narcissistic traits among Twitter users.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Social media has provided a platform for people to share views and opinions they identify with or which are significant to them. Similarly, social media enables individuals to express themselves authentically and divulge their personal experiences in a variety of ways. This behaviour, in turn, reflects the user’s personality. Social media has in recent times been used to perpetuate various forms of crimes, and a narcissistic personality trait has been linked to violent criminal activities. This negative side effect of social media calls for multiple ways to respond and prevent damage instigated. Eysenck's theory on personality and crime postulated that various forms of crime are caused by a mixture of environmental and neurological causes. This theory suggests certain people are more likely to commit a crime, and personality is the principal factor in criminal behaviour. Twitter is a widely used social media platform for sharing news, opinions, feelings, and emotions by users. Given that narcissists have an inflated self-view and engage in a variety of strategies aimed at bringing attention to themselves, features unique to Twitter are more appealing to narcissists than those on sites such as Facebook. This study adopted design science research methodology to develop a fuzzy-based machine learning predictive model to identify traces of narcissism from Twitter using data obtained from the activities of a user. Performance evaluation of various classifiers was conducted and an optimal classifier with 95% accuracy was obtained. The research found that the size of the dataset and input variables have an influence on classifier accuracy. In addition, the research developed an updated process model and recommended a research model for narcissism classification

The new curators : bloggers, fans and classic cinema on Tumblr

textThis study examines the role of social media in maintaining interest in classic cinema in today’s media culture. Ethnographic analyses and case studies were performed within a robust classic cinema fan network on the social media blogging site Tumblr. The practices of these bloggers and their online platform are framed against the traditional structure of the curator and museum, indicating that they serve many of the cultural functions attributed to these institutions. This study further demonstrates that these Tumblr networks serve as a resource for young people to discover, share, and create communities relating to classic cinema. Due to the networking capabilities of Tumblr’s youth-oriented platform, these fan activities reach a broad range of individuals, exposing them to scenes and actors from classic cinema, stimulating interest in and acceptance of the cinematic framework of classic films. This content visibility and distribution potentially draws those outside the community into the extant fan network. These communities and practices represent previously unexplored methods by which classic cinema appreciation may develop and thrive within the fast-paced media culture of the 21st century.Radio-Television-Fil

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

GPT-4V(ision) as A Social Media Analysis Engine

Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information

ICT USE AND IDENTITY FORMATION AMONG BEIJING'S RURAL-TO-URBAN GRADUATE WORKERS

Master'sMASTER OF ART

Understanding the bi-directional relationship between analytical processes and interactive visualization systems

Interactive visualizations leverage the human visual and reasoning systems to increase the scale of information with which we can effectively work, therefore improving our ability to explore and analyze large amounts of data. Interactive visualizations are often designed with target domains in mind, such as analyzing unstructured textual information, which is a main thrust in this dissertation. Since each domain has its own existing procedures of analyzing data, a good start to a well-designed interactive visualization system is to understand the domain experts' workflow and analysis processes. This dissertation recasts the importance of understanding domain users' analysis processes and incorporating such understanding into the design of interactive visualization systems. To meet this aim, I first introduce considerations guiding the gathering of general and domain-specific analysis processes in text analytics. Two interactive visualization systems are designed by following the considerations. The first system is Parallel-Topics, a visual analytics system supporting analysis of large collections of documents by extracting semantically meaningful topics. Based on lessons learned from Parallel-Topics, this dissertation further presents a general visual text analysis framework, I-Si, to present meaningful topical summaries and temporal patterns, with the capability to handle large-scale textual information. Both systems have been evaluated by expert users and deemed successful in addressing domain analysis needs. The second contribution lies in preserving domain users' analysis process while using interactive visualizations. Our research suggests the preservation could serve multiple purposes. On the one hand, it could further improve the current system. On the other hand, users often need help in recalling and revisiting their complex and sometimes iterative analysis process with an interactive visualization system. This dissertation introduces multiple types of evidences available for capturing a user's analysis process within an interactive visualization and analyzes cost/benefit ratios of the capturing methods. It concludes that tracking interaction sequences is the most un-intrusive and feasible way to capture part of a user's analysis process. To validate this claim, a user study is presented to theoretically analyze the relationship between interactions and problem-solving processes. The results indicate that constraining the way a user interacts with a mathematical puzzle does have an effect on the problemsolving process. As later evidenced in an evaluative study, a fair amount of high-level analysis can be recovered through merely analyzing interaction logs

The emerging landscape of Social Media Data Collection: anticipating trends and addressing future challenges

[spa] Las redes sociales se han convertido en una herramienta poderosa para crear y compartir contenido generado por usuarios en todo internet. El amplio uso de las redes sociales ha llevado a generar una enorme cantidad de información, presentando una gran oportunidad para el marketing digital. A través de las redes sociales, las empresas pueden llegar a millones de consumidores potenciales y capturar valiosos datos de los consumidores, que se pueden utilizar para optimizar estrategias y acciones de marketing. Los beneficios y desafíos potenciales de utilizar las redes sociales para el marketing digital también están creciendo en interés entre la comunidad académica. Si bien las redes sociales ofrecen a las empresas la oportunidad de llegar a una gran audiencia y recopilar valiosos datos de los consumidores, el volumen de información generada puede llevar a un marketing sin enfoque y consecuencias negativas como la sobrecarga social. Para aprovechar al máximo el marketing en redes sociales, las empresas necesitan recopilar datos confiables para propósitos específicos como vender productos, aumentar la conciencia de marca o fomentar el compromiso y para predecir los comportamientos futuros de los consumidores. La disponibilidad de datos de calidad puede ayudar a construir la lealtad a la marca, pero la disposición de los consumidores a compartir información depende de su nivel de confianza en la empresa o marca que lo solicita. Por lo tanto, esta tesis tiene como objetivo contribuir a la brecha de investigación a través del análisis bibliométrico del campo, el análisis mixto de perfiles y motivaciones de los usuarios que proporcionan sus datos en redes sociales y una comparación de algoritmos supervisados y no supervisados para agrupar a los consumidores. Esta investigación ha utilizado una base de datos de más de 5,5 millones de colecciones de datos durante un período de 10 años. Los avances tecnológicos ahora permiten el análisis sofisticado y las predicciones confiables basadas en los datos capturados, lo que es especialmente útil para el marketing digital. Varios estudios han explorado el marketing digital a través de las redes sociales, algunos centrándose en un campo específico, mientras que otros adoptan un enfoque multidisciplinario. Sin embargo, debido a la naturaleza rápidamente evolutiva de la disciplina, se requiere un enfoque bibliométrico para capturar y sintetizar la información más actualizada y agregar más valor a los estudios en el campo. Por lo tanto, las contribuciones de esta tesis son las siguientes. En primer lugar, proporciona una revisión exhaustiva de la literatura sobre los métodos para recopilar datos personales de los consumidores de las redes sociales para el marketing digital y establece las tendencias más relevantes a través del análisis de artículos significativos, palabras clave, autores, instituciones y países. En segundo lugar, esta tesis identifica los perfiles de usuario que más mienten y por qué. Específicamente, esta investigación demuestra que algunos perfiles de usuario están más inclinados a cometer errores, mientras que otros proporcionan información falsa intencionalmente. El estudio también muestra que las principales motivaciones detrás de proporcionar información falsa incluyen la diversión y la falta de confianza en las medidas de privacidad y seguridad de los datos. Finalmente, esta tesis tiene como objetivo llenar el vacío en la literatura sobre qué algoritmo, supervisado o no supervisado, puede agrupar mejor a los consumidores que proporcionan sus datos en las redes sociales para predecir su comportamiento futuro

An Investigation into the Influence of Social Media Message Context on Retailer-Consumer Interaction: A Case Study from the Lens of a UK Retailer

This thesis investigates social media and retailer-consumer interaction with a research site of a well-established medium sized specialist retailer within the outdoor activities sector (the case organisation), selling goods and services online, and offline from physical retail stores across the United Kingdom (UK). The research investigates the case organisation’s response to the development of social media channels, with the purpose of developing understanding of the influence of content posted by the retailer on Twitter and Facebook platforms. The ease of access of these social media communications allowed the researcher to freely view the context of the case organisation’s activity and helped shape the questioning of research participants in their face-to-face semi-structured interview. The research aimed to develop understanding, and therefore qualitative methods were most appropriate. The philosophical assumptions were for a subjectivist ontology and interpretivist epistemology. The theoretical framework of uses and gratification theory (UGT), provided a priori themes to identify the retailer’s postings into social, entertainment or information value to the consumer. This study demonstrates that the case organisation’s posting activity on its primary Twitter and Facebook accounts, were predominantly of information value to the consumer, whereas users appeared to interact more with postings that were of social or entertainment value. The apparent under resourcing of the specialist product sub-community accounts (S1 and S2) appears to be suppressing social media activity, and thereby interaction with community members. But by reassigning management of S1 and S2 activity to generalists within the social media team, these research findings indicate that the case organisation is putting the close ingroup interaction that these sub-communities serve at risk. The one store-based subcommunity Facebook account (S3) was achieving a more balanced mix of user interaction than the case organisation’s primary account; indicating that local staff involvement was a motivating factor in consumer interaction. These research findings indicate that by re-evaluating the context of messages posted on its primary Twitter and Facebook accounts, and the involvement of local store account activity, consumer interaction on these channels will increase. Furthermore, the research findings suggest that by developing a transparent corporate social media strategy, that includes clear policy and operating procedures, those actors on the periphery of social media activity will benefit from the resultant clarity of understanding. And the call for training in managing social media activity for business by these actors can be addressed and delivered within the framework of a robust social media strategy. While there are inherent limitations in researching a single-case organisation, the generous access granted to the researcher provided a unique opportunity to investigate the research aim and objectives in a real-world setting. Moreover, this indepth study of Twitter and Facebook activity at the case organisation contributes to theory and practice by providing new insights and understanding on the influence of message context on consumer interaction from the lens of a specialist retailer