Measurement and Analysis of the Swarm Social Network With Tens of Millions of Nodes

Yang, Chen; Hu, Jiawen; Zhao, Hao; Xiao, Yu; Hui, Pan

doi:10.1109/access.2018.2789915

Cited by 22 publications

(9 citation statements)

References 47 publications

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“…This application, initiated by Foursquare in May 2014, enables users to share places they visit on Twitter and Facebook based on Foursquare venues. According to a recent comprehensive empirical analysis of the Foursquare network by Chen, Hu, Zhao, Xiao, and Hui (2018), Swarm and Foursquare had around 60 M monthly active users (51.07% male, 41.43% female, and 7.50% undisclosed), compared to 330 M monthly active users on Twitter. Among all Swarm users 56.76% shared their locations on Twitter/Facebook.…”

Section: Methodsmentioning

confidence: 99%

On the causal relation between real world activities and emotional expressions of social media users

Falavarjani

Jovanović

Fani

et al. 2020

Asso for Info Science & Tech

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Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact—the effect of real‐world activities on online actions—has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi‐experimental design study that examined the presence of causal relations between real‐world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check‐ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi‐experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions.

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Section: Methodsmentioning

confidence: 99%

On the causal relation between real world activities and emotional expressions of social media users

Falavarjani

Jovanović

Fani

et al. 2020

Asso for Info Science & Tech

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“…These two apps share the same user database, but with a different focus. The Swarm app [11] supports the check-in function and provides a life-logging service. Differently, in the current version of Foursquare, tips is the primary form of UGC.…”

Section: Background and Data Collectionmentioning

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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“…Pengambilan data ini memerlukan sumber daya dan waktu yang memadai [18]. Beberapa aplikasi media sosial memberikan persyaratan tertentu untuk melakukan data crawling menggunakan API, seperti pada Facebook dan Twitter [19], [20]. Penelitian ini diawali dari membangun sebuah aplikasi berbahasa Python yang terkoneksi dengan fitur Search Engine dari Twitter.…”

Section: A Pengumpulan Dataunclassified

Klasifikasi Interaksi Kampanye di Media Sosial Menggunakan Naïve Bayes Kernel Estimator

Nugroho

Hidayatillah

Sumpeno

et al. 2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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The development of technology also influences changes in campaign patterns. Campaign activities are part of the process of Election of Regional Heads. The aim of the campaign is to mobilize public participation, which is carried out directly or through social media. Social media becomes a channel for interaction between candidates and their supporters. Interactions that occur during the campaign period can be one indicator of the success of the closeness between voters and candidates. This study aims to get the pattern of campaign interactions that occur on Twitter social media channels. This interaction pattern is classified as a model in measuring the success of campaigns on social media. The research begins with obtaining data through the data retrieval process using the API feature provided by Twitter. Furthermore, pre-processing is carried out before data can be processed in an algorithmic method. This stage is done to improve data quality so as to improve accuracy. Naive Bayes Classifier was chosen because of a simple procedure, then Kernel Estimator (KE) was used to improve performance. The use of naive Bayes Kernel Estimator can improve model performance from 76.74% to 80.14%. Testing models with split percentage methods on several combinations get satisfactory results.

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Measurement and Analysis of the Swarm Social Network With Tens of Millions of Nodes

Cited by 22 publications

References 47 publications

On the causal relation between real world activities and emotional expressions of social media users

On the causal relation between real world activities and emotional expressions of social media users

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

Klasifikasi Interaksi Kampanye di Media Sosial Menggunakan Naïve Bayes Kernel Estimator

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