Understanding Social Media Users via Attributes and Links

abstract: With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users. Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Mining User Knowledge for Investigating the Facebook Business Model: The Case of Taiwan Users

[[abstract]]Social network sites (SNS), as web-based services, allow users to make open or semi-open profiles within the systems they are part of, to see lists of other people in the group and to see the relations of people within different groups. Facebook is essentially an online social network site in which individuals can share photographs, personal information, and join groups of friends. This study investigates the experiences on Facebook of various users in Taiwan. Their degrees of confidence were often demonstrated by word-of-mouth disseminations about the social network site. Further, this research looks at how the reputations of Facebook proprietors and their affiliates were disseminated through relationship marketing for formulated social network marketing in its business model concerns. Therefore, this study uses the a priori algorithm as an association rules approach, and cluster analysis for data mining. We divide Facebook users into two groups of contributors and lurkers by their profiles and then find each group’s social network community information utilization and online purchase behaviors for investigating the Facebook business models.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures

Soccer on Social Media

In the era of digitalization, social media has become an integral part of our lives, serving as a significant hub for individuals and businesses to share information, communicate, and engage. This is also the case for professional sports, where leagues, clubs and players are using social media to reach out to their fans. In this respect, a huge amount of time is spent curating multimedia content for various social media platforms and their target users. With the emergence of Artificial Intelligence (AI), AI-based tools for automating content generation and enhancing user experiences on social media have become widely popular. However, to effectively utilize such tools, it is imperative to comprehend the demographics and preferences of users on different platforms, understand how content providers post information in these channels, and how different types of multimedia are consumed by audiences. This report presents an analysis of social media platforms, in terms of demographics, supported multimedia modalities, and distinct features and specifications for different modalities, followed by a comparative case study of select European soccer leagues and teams, in terms of their social media practices. Through this analysis, we demonstrate that social media, while being very important for and widely used by supporters from all ages, also requires a fine-tuned effort on the part of soccer professionals, in order to elevate fan experiences and foster engagement

Exploring the relation between consumer motivations & engagement with brands in Facebook

This thesis aims to explore the digital consumer behavior and proposes a conceptual framework that allows to comprehend the consumer’s Motivations and Engagement types to interact with brands in Facebook. Specifically, the research goal is to explore the relationship between the two and point out which Motivations better explain Engagement. Lastly, the analysis consists of segmenting Facebook users based on their Motivations and exploring their Engagement levels. The present research addresses the Portuguese Facebook users’ behavior based on Motivational and Engagement variables. Those were chosen with the intention of recognizing their importance in this context and exploring the connection between both. Scales from previous literatures were adapted and used to explore the Motivations, Enginkaya and Yilmaz (2014), and the Engagement, Malciute (2012). A quantitative and exploratory study was conducted and an online questionnaire was applied to a convenience sample of 350 Facebook users. Results indicated that the main Motivations to interact with brands in Facebook are Opportunity Seeking, Conversation and Entertainment. Moreover, the main Consumer Engagement dimension is Emotional. Further, there is a significant relation between Motivations and Engagement, and the Motivations that better help to predict Engagement are Brand Affiliation, Entertainment and Investigation. Moreover, three segments of Facebook users were identified and the main one presents the highest Engagement levels. The framework might serve as a tool for managers to better understand Facebook users’ behaviors regarding brands, thus enabling them to improve the allocation of digital resources, especially regarding Facebook and their marketing strategies with a suitable segmentation approach.Esta tese tem como objetivo explorar o comportamento do consumidor digital e propõe um quadro conceptual que visa facilitar a compreensão das Motivações que levam o consumidor a interagir com as marcas no “Facebook” e o seu “Engagement”. A intenção fulcral desta pesquisa é investigar a relação entre Motivações e “Engagement” e realçar as Motivações que melhor explicam o “Engagement”. O propósito final é segmentar os usuários de “Facebook” consoante as suas Motivações e explorar o seu nível de “Engagement”. A presente pesquisa relativa aos usuários do “Facebook” portugueses tem como base variáveis de Motivação e “Engagement”, sendo que as mesmas foram retiradas e adaptadas do estudo de Enginkaya e Yilmaz (2014) e do de Malciute (2012), respetivamente. Estas variáveis foram selecionadas com a finalidade de verificar a sua importância neste contexto e explorar a relação entre ambas. Um estudo quantitativo e exploratório foi elaborado. Foi aplicado um questionário “online” a uma amostra de 350 usuários de “Facebook”. Os resultados indicam que as principais Motivações são: Procura de Oportunidades, Conversacional e Entretenimento. Relativamente ao “Engagement” a dimensão com maior relevância é a Emocional. Os resultados comprovam a relação entre Motivações e “Engagement” e destacam a Filiação às Marcas, o Entretenimento e a Investigação como sendo as Motivações, que melhor explicam o “Engagement”. O quadro conceptual poderá assim servir como ferramenta para que as marcas compreendam o comportamento do consumidor “facebookiano”, tornando mais eficientes a alocação de recursos “online” e estratégias de marketing com uma boa abordagem de segmentação