Emergence of Equilibria from Individual Strategies in Online Content Diffusion

Social scientists have observed that human behavior in society can often be modeled as corresponding to a threshold type policy. A new behavior would propagate by a procedure in which an individual adopts the new behavior if the fraction of his neighbors or friends having adopted the new behavior exceeds some threshold. In this paper we study the question of whether the emergence of threshold policies may be modeled as a result of some rational process which would describe the behavior of non-cooperative rational members of some social network. We focus on situations in which individuals take the decision whether to access or not some content, based on the number of views that the content has. Our analysis aims at understanding not only the behavior of individuals, but also the way in which information about the quality of a given content can be deduced from view counts when only part of the viewers that access the content are informed about its quality. In this paper we present a game formulation for the behavior of individuals using a meanfield model: the number of individuals is approximated by a continuum of atomless players and for which the Wardrop equilibrium is the solution concept. We derive conditions on the problem's parameters that result indeed in the emergence of threshold equilibria policies. But we also identify some parameters in which other structures are obtained for the equilibrium behavior of individuals

Curation-based network marketing: strategies for network growth and electronic word-of-mouth diffusion

In the last couple of years, a new aspect of online social networking has emerged, in which the strength of social network connections is based not on social ties but mutually shared interests. This dissertation studies these "curation-based" online social networks (CBN) and their suitability for the diffusion of electronic word-of-mouth information (eWOM). Within CBN, users do not rely on profiles full of personal information to identify network ``friends''. Rather, CBN users curate collections of digital content that becomes their digital self-expression within the network. This digital content can then be viewed, commented on, and shared across the pages of other CBN users. As the dissertation will show, this process of digital content curation, a relatively new online practice that centers around the collection and sharing of rich digital media, builds CBN, and presents exciting opportunities for the study of eWOM. The dissertation presents three studies around digital content curation, CBN, and eWOM diffusion. Study 1 examines individual level antecedents of digital content curation behavior. In this study, we use theory from sociology and behavioral psychology to develop a model of user intentions towards digital content curation behavior. We find that digital content curation is comprised of a mixture of social and utilitarian motivations, and that the management and organization of digital content is a major reason that people spend time on CBN. Study 2 examines the way that digital content curation behaviors grow CBN. We study a sample of 1800 CBN users to determine the way that their digital content curation behaviors attract and retain interested CBN followers. We find that the most successful CBN users are those that can generate an eWOM response around their content collections. Additionally, we find that textual eWOM plays a very limited role in attracting followers in the CBN environment. Finally, Study 3 examines eWOM diffusion by analyzing data on the structure and diffusion of digital content through real-world CBN network structures. This descriptive analysis of eWOM in CBN presents details on the way that CBN data is structured, and the methods and techniques that can be used to collect and analyze real-world eWOM collected from a CBN site. The study uses the UCINET network visualization software package to examine the networks of thirty companies operating CBN pages. Using a unique data set specifically compiled for this study, we are able to visualize the diffusion of curated digital content through the networks of these companies, and show how companies can identify their most influential followers as targets for further eWOM and traditional marketing efforts. Together, the three dissertation studies offer a holistic view of content curation behavior and curation-based online social networking and has the potential to fill the gap in the literature on information diffusion and online marketing. We make substantial contributions to the areas of sociology, economics, and marketing, and offer one of the first treatments of the role of digital content curation in online social networks

A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks

The preferences of Web information purchasers are rapidly evolving. Cost-effectiveness is now becoming less regarded than cost-satisfaction, which emphasizes the purchaser’s psychological satisfaction. One method to improve a user’s cost-satisfaction in multimedia content retrieval is to utilize the mood inherent in multimedia items. An example of applications using this method is SNS (Social Network Services), which is based on folksonomy, but its applications encounter problems due to synonyms. In order to solve the problem of synonyms in our previous study, the mood of multimedia content is represented with arousal and valence (AV) in Thayer’s two-dimensional model as its internal tag. Although some problems of synonyms could now be solved, the retrieval performance of the previous study was less than that of a keyword-based method. In this paper, a new method that can solve the synonym problem is proposed, while simultaneously maintaining the same performance as the keyword-based approach. In the proposed method, a mood of multimedia content is represented with a fuzzy set of 12 moods of the Thayer model. For the analysis, the proposed method is compared with two methods, one based on AV value and the other based on keyword. The analysis results demonstrate that the proposed method is superior to the two methods

Estimating Emotion Contagion on Social Media via Localized Diffusion in Dynamic Graphs

We present a computational approach for estimating emotion contagion on social media networks. Built on a foundation of psychology literature, our approach estimates the degree to which the perceivers' emotional states (positive or negative) start to match those of the expressors, based on the latter's content. We use a combination of deep learning and social network analysis to model emotion contagion as a diffusion process in dynamic social network graphs, taking into consideration key aspects like causality, homophily, and interference. We evaluate our approach on user behavior data obtained from a popular social media platform for sharing short videos. We analyze the behavior of 48 users over a span of 8 weeks (over 200k audio-visual short posts analyzed) and estimate how contagious the users with whom they engage with are on social media. As per the theory of diffusion, we account for the videos a user watches during this time (inflow) and the daily engagements; liking, sharing, downloading or creating new videos (outflow) to estimate contagion. To validate our approach and analysis, we obtain human feedback on these 48 social media platform users with an online study by collecting responses of about 150 participants. We report users who interact with more number of creators on the platform are 12% less prone to contagion, and those who consume more content of `negative' sentiment are 23% more prone to contagion. We will publicly release our code upon acceptance

Open Large-Scale Online Social Network Dyn

Online social networks have quickly become the most popular destination on the World Wide Web. These networks are still a fairly new form of online human interaction and have gained wide popularity only recently within the past three to four years. Few models or descriptions of the dynamics of these systems exist. This is largely due to the difficulty in gaining access to the data from these networks which is often viewed as very valuable. In these networks, members maintain list of friends with which they share content with by first uploading it to the social network service provider. The content is then distributed to members by the service provider who generates a feed for each member containing the content shared by all of the member's friends aggregated together. Direct access to dynamic linkage data for these large networks is especially difficult without a special relationship with the service provider. This makes it difficult for researchers to explore and better understand how humans interface with these systems. This dissertation examines an event driven sampling approach to acquire both dynamics link event data and blog content from the site known as LiveJournal. LiveJournal is one of the oldest online social networking sites whose features are very similar to sites such as Facebook and Myspace yet smaller in scale as to be practical for a research setting. The event driven sampling methodology and analysis of the resulting network model provide insights for other researchers interested in acquiring social network dynamics from LiveJournal or insight into what might be expected if an event driven sampling approach was applied to other online social networks. A detailed analysis of both the static structure and network dynamics of the resulting network model was performed. The analysis helped motivated work on a model of link prediction using both topological and content-based metrics. The relationship between topological and content-based metrics was explored. Factored into the link prediction analysis is the open nature of the social network data where new members are constantly joining and current members are leaving. The data used for the analysis spanned approximately two years

Integrated approach to detect spam in social media networks using hybrid features

Online social networking sites are becoming more popular amongst Internet users. The Internet users spend some amount of time on popular social networking sites like Facebook, Twitter and LinkedIn etc. Online social networks are considered to be much useful tool to the society used by Internet lovers to communicate and transmit information. These social networking platforms are useful to share information, opinions and ideas, make new friends, and create new friend groups. Social networking sites provide large amount of technical information to the users. This large amount of information in social networking sites attracts cyber criminals to misuse these sites information. These users create their own accounts and spread vulnerable information to the genuine users. This information may be advertising some product, send some malicious links etc to disturb the natural users on social sites. Spammer detection is a major problem now days in social networking sites. Previous spam detection techniques use different set of features to classify spam and non spam users. In this paper we proposed a hybrid approach which uses content based and user based features for identification of spam on Twitter network. In this hybrid approach we used decision tree induction algorithm and Bayesian network algorithm to construct a classification model. We have analysed the proposed technique on twitter dataset. Our analysis shows that our proposed methodology is better than some other existing techniques

Service implementation network engagement: An Indicator of policy advocacy?

Service implementation network engagement and its effects on the likelihood that organizations will engage in collaborative policy advocacy is studied. The research was based on a centrally governed mental health service network in Bernalillo County New Mexico. The study population consists of one for-profit network administrative organization (NAO) contracted by the state and 33 mental health service providers representing all three sectors. Based on earlier research, several hypotheses are developed regarding the roles of resource dependence, organizational embeddedness in cliques, and the bonding model of network organizing. The hypotheses are tested using multiple regression quadratic assignment procedure (MRQAP). The study generated two important findings. First, similar dependence on the NAO, based on service linkages, is found to be negatively related to policy advocacy. Second, those specific organizations who both share a clique with the NAO and who are similarly dependent upon the NAO find themselves to be more likely to engage in dyadic relations of policy advocacy. v Based on limited interview data, a qualitative analysis was undertaken. This analysis, more specifically, attempts to shine some light on the content of policy advocacy. The analysis finds that organizational decisions to engage in policy advocacy can be best understood from a bottom up approach starting with the development of social capital and understanding the dynamics of resource dependence in a centrally governed network. This analysis is followed up with a discussion, focusing on theoretical and practical implications and suggestions for further research

Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach

This study addressed the complex task of sentiment analysis on a dataset of 119,988 original tweets from Weibo using a Convolutional Neural Network (CNN), offering a new approach to Natural Language Processing (NLP). The data, sourced from Baidu's PaddlePaddle AI platform, were meticulously preprocessed, tokenized, and categorized based on sentiment labels. A CNN-based model was utilized, leveraging word embeddings for feature extraction, and trained to perform sentiment classification. The model achieved a macro-average F1-score of approximately 0.73 on the test set, showing balanced performance across positive, neutral, and negative sentiments. The findings underscore the effectiveness of CNNs for sentiment analysis tasks, with implications for practical applications in social media analysis, market research, and policy studies. The complete experimental content and code have been made publicly available on the Kaggle data platform for further research and development. Future work may involve exploring different architectures, such as Recurrent Neural Networks (RNN) or transformers, or using more complex pre-trained models like BERT, to further improve the model's ability to understand linguistic nuances and context