What Drives Users to Follow Companies’ Microblogs?: An Elaboration Likelihood Model Perspective

Along with the prevalence of microblogging technology, many companies have been creating microblog accounts to promote their products/brands and communicate with customers. However, it is still unclear regarding what factors are critical and can drive users to follow companies’ microblogs. To fill this gap, the present research develops a research model through the perspective of elaboration likelihood model. According to the elaboration level of information processing, we explicate users’ following behavior through three levels of participation: reading messages, forwarding messages, and commenting on messages of companies’ microblogs. We propose that information quality (the central route variable) and source credibility (the peripheral route variable) are two important antecedents in the research model. In addition, we extend the model by considering the role of similarity and examining its impacts on users’ following behavior. We empirically test our research model by collecting data from an existing microblogging site in China. The results show that most of the proposed hypotheses are supported. We thereafter discuss these findings, point out limitations and opportunities for future research, and summarize this study with implications for both theory and practice

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

Customers\u27 demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management

Factors Influencing Users\u27 Reading Satisfaction of Articles on WeChat

As social media, such as WeChat, develops rapidly, huge amounts of information are becoming more easily accessible. However, both the limited time and short attention span of people lead to competition among articles, and a large number of them end up being buried in the sea of information. Based on the ELM theory, this paper analyzes the factors influencing users’ reading satisfaction of articles. The results show that the title information amount, the title activeness and the readability of the article positively affect the reading satisfaction of users. The amount of information in an article has a negative influence on the satisfaction of users. It is important for an article to have an appropriate number of pictures and be published at a specific time period. Furthermore, use of advertising and marketing vocabulary in articles will reduce users’ reading satisfaction. Finally, relevant theoretical and practical implications are discussed

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Social media influence analysis, sometimes also called authority detection, aims to rank users based on their influence scores in social media. Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users’ influence scores. They rarely consider a person’s expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally ”Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. Our analysis gave a good explanation of the phenomenon of “top across-domain influencers”. In addition, different expertise levels showed influence variation patterns: e.g., (1) high-expertise celebrities have stronger influence on the “audience” in their expertise domains; (2) expertise seems to be more important than relevance and participation for social media influence; (3) the audiences of top expertise celebrities are more likely to forward tweets on topics outside the expertise domains from high-expertise celebrities

A Multilayer Naïve Bayes Model for Analyzing User’s Retweeting Sentiment Tendency

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user’s retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user’s network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user’s retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks

Analyzing the strength of ties of Retweet in health domain

Social Network (SN) is created whenever people interact with other people. Online SN gained considerable popularity in the last years such as Fa- cebook, Twitter and etc Twitter is SN and microblogging service that creates some interesting social network structures - follow relationships. Users follow someone mostly because they share common interests and they may exchange messages called tweets. If a user post a tweet, if their follower like it they repost it or retweet it. In this context, we aim to explore and study the topological structure of user‟s retweet network, as well, new scaling measures based on strength of retweet ties. The findings suggested that relations of “friendship” are important but not enough to find out how important users are. We uncovered other some principles that must be studied like, homophily phenomenon. Ho- mophily explores properties of social network relationships, i.e. the preference for associating with individuals of the same background. Last but not least, it is worth emphasizing that we uncovered a weak correlation between Degree Cen- trality and Betweenness Centrality (49 percent) in Retweet-network and strong correlation between Degree and Betweenness centrality in Follower-network (89 percent). These find suggests that retweet network may have some fractal properties

Information propagation in social networks during crises: A structural framework

In crisis situations like riots, earthquakes, storms, etc. information plays a central role in the process of organizing interventions and decision making. Due to their increasing use during crises, social media (SM) represents a valuable source of information that could help obtain a full picture of people needs and concerns. In this chapter, we highlight the importance of SM networks in crisis management (CM) to show how information is propagated through. The chapter also summarizes the current state of research related to information propagation in SMnetworks during crises. In particular three classes of information propagation research categories are identified: network analysis and community detection, role and topic-oriented information propagation, and infrastructure-oriented information propagation. The chapter describes an analysis framework that deals with structural information propagation for crisismanagement purposes. Structural propagation is about broadcasting specific information obtained from social media networks to targeted sinks/receivers/hubs like emergency agencies, police department, fire department, etc. Specifically, the framework aims to identify the discussion topics, known as sub-events, related to a crisis (event) from SM contents. A brief description of techniques used to detect topics and the way those topics can be used in structural information propagation are presented