The power of comments: fostering social interactions in microblog networks

Wang, Tianyi; Chen, Yang; Wang, Yi; Wang, Bolun; Wang, Gang; Li, Xing; Zheng, Haitao; Zhao, Ben Y.

doi:10.1007/s11704-016-5198-y

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…These people influence the buyer's behavior by writing fake reviews and harm the brand's reputation as well (Savage, Zhang, Yu, Chou, & Wang, 2014). T. Wang et al (2016) have presented the impact of comments in microblogging sites to detect malicious users. G. Wu, Greene, Smyth, and Cunningham (2010) have proposed a method for shill detection.…”

Section: Anomaly Detection and Privacy Preservationmentioning

confidence: 99%

Role of sentiment analysis in social media security and analytics

Sharma

Jain

2020

WIREs Data Min & Knowl

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Social media, in recent times, has with eased an explosion of data with so many social media platforms available to interact and express opinions freely. This has led to easy access to the privacy of social media users which raise broader security concerns and issues. The present paper provides an overview of various sentiment analysis approaches and techniques for social media security and analytics. The multiple security application domains like deception detection, anomaly detection, risk management, and disaster relief have been identified where sentiment analysis is used for social media security. An indepth study on security issues related to data provenance, distrust, e-commerce security, consumer security breaches, market surveillance, credibility, and risk assessment in social media have been presented. A comparison of various techniques, methodologies, dataset, and application domain where sentiment analysis is used has been discussed. The present work discusses the results of different machine learning techniques based on the performance metrics that have been used for the implementation of sentiment analysis in the respective security domains. It identifies the various gaps, issues, and the recent advancements in the field and presents a line of work that needs to be carried forward in future.

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Section: Anomaly Detection and Privacy Preservationmentioning

confidence: 99%

Role of sentiment analysis in social media security and analytics

Sharma

Jain

2020

WIREs Data Min & Knowl

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“…Othmane proposes a time series model for measuring the privacy of information disseminated in an online social network, considering the factors including relationship strength between communicating users and influence of friends. And Wang studied fostering social interactions between users in microblog networks …”

Section: Related Workmentioning

confidence: 99%

“…And Wang studied fostering social interactions between users in microblog networks. 14 A game theoretical approach is used to analyze online interaction behavior. Network information disseminators have different interest preferences, driving them to disseminate information based on various purposes, thus formulate a game between publishers and forwarders.…”

Section: Related Workmentioning

confidence: 99%

A SECPG model for purchase behavior analysis in social e‐commerce environment

Liu

Wang

et al. 2020

Int J Communication

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Summary In social e‐commerce environment, consumers share commodity information with others frequently. It is urgent for social e‐commerce enterprises or merchants to learn rules about commodity information dissemination so as to improve the purchase rate of goods and predict the sales trend at the same time. In this paper, we extend the social e‐commerce customers' purchasing behavior game model (SECPG model) to simulate multiple complex social e‐commerce networks, so as to further quantify internal relation between users' information dissemination and online shopping behavior to a certain extent and predict the purchase rate in diverse social e‐commerce networks. The concept of relationship update frequency is introduced to the updating mechanism in social e‐commerce, and simulations are carried out for networks with various update frequency of social relations. Numerical simulations show that individuals' reputation and cost‐to‐benefit ratio both have specific ranges of influence on the purchase rate in social e‐commerce environment. And relationship update frequency is also a key factor for users to spread purchase information.

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“…As in Eq. (1), we calculate the balance metric for each user [58]. It defines the ratio of the number of followers to the number of followings.…”

Section: A Social Graph Analysismentioning

confidence: 99%

Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global Scale

Chen

Xiao

et al. 2020

IEEE Trans. Comput. Soc. Syst.

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Being a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect the user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this paper, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found a number of unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected components, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users, then look into the tip contents from the perspectives of tip venues, temporal patterns and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to her profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users, and are constructive for different relevant entities, including LBSN service providers, Internet service providers and third-party application providers.

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The power of comments: fostering social interactions in microblog networks

Cited by 13 publications

References 47 publications

Role of sentiment analysis in social media security and analytics

Role of sentiment analysis in social media security and analytics

A SECPG model for purchase behavior analysis in social e‐commerce environment

Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global Scale

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