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The Relationship between Online Social Network Ties and User Attributes

Published: 07 May 2019 Publication History

Abstract

The distance between users has an effect on the formation of social network ties, but it is not the only or even the main factor. Knowing all the features that influence such ties is very important for many related domains such as location-based recommender systems and community and event detection systems for online social networks (OSNs). In recent years, researchers have analyzed the role of user geo-location in OSNs. Researchers have also attempted to determine the probability of friendships being established based on distance, where friendship is not only a function of distance. However, some important features of OSNs remain unknown. In order to comprehensively understand the OSN phenomenon, we also need to analyze users’ attributes. Basically, an OSN functions according to four main user properties: user geo-location, user weight, number of user interactions, and user lifespan. The research presented here sought to determine whether the user mobility pattern can be used to predict users’ interaction behavior. It also investigated whether, in addition to distance, the number of friends (known as user weight) interferes in social network tie formation. To this end, we analyzed the above-stated features in three large-scale OSNs. We found that regardless of a high degree freedom in user mobility, the fraction of the number of outside activities over the inside activity is a significant fraction that helps us to address the user interaction behavior. To the best of our knowledge, research has not been conducted elsewhere on this issue. We also present a high-resolution formula in order to improve the friendship probability function.

References

[1]
Martin J. Chorley, Roger M. Whitaker, and Stuart M. Allen. 2015. Personality and location-based social networks. Computers in Human Behavior 46, 5 (2015), 45--56.
[2]
Gao Xu-Rui, Wang Li, and Wu Wei-Li. 2015. An algorithm for friendship prediction on location-based social networks. In Computational Social Networks. Springer, Cham, 193--204.
[3]
Andreas Kaltenbrunner, Salvatore Scellato, Yana Volkovich, David Laniado, Dave Currie, Erik J. Jutemar, and Cecilia Mascolo. 2012. Far from the eyes, close on the web: Impact of geographic distance on online social interactions. In Proceedings of the 2012 ACM Workshop on Workshop on Online Social Networks (WOSN’12). ACM, Helsinki, 19--24.
[4]
Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: User movement location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining (KDD’11). ACM, San Diego, 1082--1090.
[5]
Longbo Kong, Zhi Liu, and Yan Huang. 2014. SPOT: Locating social media users based on social network context. VLDB Endowment 7, 13 (2014), 1681--1684.
[6]
Zhi Liu and Yan Huang. 2014. Community detection from location-tagged networks. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’14). ACM, Dallas, Texas, 525--528.
[7]
Balázs Lengyel, Attila Varga, Bence Ságvári, and Ákos Jakobi. 2013. Distance Dead or Alive: Online Social Networks from a Geography Perspective (1st ed.). International Business School, Budapest.
[8]
Balázs Lengyel, Attila Varga, Bence Ságvári, and János Kertész. 2015. Geographies of an online social network. PLOS ONE 10, 9 (2015), 1--13.
[9]
Salvatore Scellato, Anastasios Noulas, Renaud Lambiotte, and Cecilia Mascolo. 2011. Socio-spatial properties of online location-based social networks. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence, San Francisco (www.aaai.org).
[10]
David Liben-Nowelly, Jasmine Novak, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins. 2005. Geographic routing in social networks. In Proceedings of the National Academy of Sciences of the United States of America 102, 33 (2005), 11623--11628.
[11]
Statista, “Statista,” Statista. 2017. {Online}. Retrieved December 1, 2017 from https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/.
[12]
Marta C. Gonza´lez, Ce´sar A. Hidalgo, and Albert-La´szlo´ Baraba´si. 2008. Understanding individual human mobility patterns. Nature 453 (2008), 779--782.
[13]
Renaud Lambiotte, Vincent D. Blondel, Cristobald de Kerchove, Etienne Huens, Christophe Prieur, Zbigniew Smoreda, and Paul Van Dooren. 2008. Geographical dispersal of mobile telecommunication networks. Physica A: Statistical Mechanics and its Applications 387, 21 (2008), 5317--5325.
[14]
Justin Cranshaw, Eran Toch, and Jason Hong. 2010. Bridging the gap between physical location and online social networks. In Proceedings of the 12th ACM International Conference on Ubiquitous computing (UbiComp’10). Copenhagen, 119--128.
[15]
Anthony Bonato, Noor Hadi, Paul Horn, Pawel Pralat, and Changping Wang. 2011. Models of online social networks. Internet Mathematics 6, 3 (2011), 285--313.
[16]
C. Herrera-Yagüe, Christian M. Schneider, Thomas Couronné, Zbigniew Smoreda, Rosa M. Benito, Pedro J. Zufiria, and Marta C. Gonzalez. 2015. The anatomy of urban social networks and its implications in the searchability problem. Scientific Reports 5 (2015), Article 10265, 1--13.
[17]
Farseev Aleksandr, Nie Liqiang, Mohammad Akbari, and Tat-Seng Chua. 2015. Harvesting multiple sources for user profile learning: A big data study. In Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (ICMR’15). ACM, Shanghai, 235--242.
[18]
Miltiadis Allamanis, Salvatore Scellato, and Cecilia Mascolo. 2012. Evolution of a location-based online social network: Analysis and models. In Proceedings of the 2012 Internet Measurement Conference (IMC’12). ACM, Boston, 145--158.
[19]
Christo Wilson, Alessandra Sala, Krishna Puttaswamy, and Ben Y. Zhao. 2012. Beyond social graphs: User interactions in online social networks and their implications. ACM Transactions on the Web 6, 4 (2012), 17:1--17:31.
[20]
Amin Mahmoudi, Mohd Ridzwan Yaakub, and Azuraliza Abu Bakar. 2018. A new method to discretize time to identify the milestones of online social networks. Social Network Analysis and Mining 8, 34 (2018), 34:1--34:20.
[21]
J. Leskovec, J. Kleinberg, and C. Faloutsos. 2005. Graphs over time: Densification laws, shrinking diameters and possible explanations. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). Chicago, 177--187.
[22]
Mohamed Sarwat, Justin J. Levandoski, Ahmed Eldawy, and Mohamed F. Mokbel. 2014. LARS*: An efficient and scalable location-aware recommender system. IEEE Transactions on Knowledge and Data Engineering 26, 6 (2014), 1384--1399.
[23]
Jason Scott. “archive.org,” Internet Archive, 8 October 2013. {Online}. Retrieved July 15, 2018 from https://archive.org/details/201309_foursquare_dataset_umn.
[24]
worldometers, “worldometers,” worldometers. 2018. {Online}. Retrieved July 3, 2018 from http://www.worldometers.info/population/largest-cities-in-the-world/.
[25]
Amin Mahmoudi, Mohd Ridzwan, and Azuraliza. 2018. New time-based model to identify the influential users in online social networks. Program 52, 2 (2018), 278--290.
[26]
M. Zubair Shafiq, Muhammad U. Ilyas, Alex X. Liu, and Hayder Radha. 2013. Identifying leaders and followers in online social networks. IEEE Journal on Selected Areas in Communications 31, 9 (2013), 618--628.
[27]
Reza Zafarani, Lei Tang, and Huan Liu. 2015. User identification across social media. ACM Transactions on Knowledge Discovery from Data 10, 2 (2015), 16:1--16:30.
[28]
Fei Gao, Katarzyna Musial, Colin Cooper, and Sophia Tsoka. 2015. Link prediction methods and their accuracy for different social networks and network metrics. Scientific Programming 2015 (2015), Article 172879, 1--13.
[29]
Fariya Sharmeen and H. J. P. Timmermans. 2011. Effects of residential move on interaction frequency with social network. In Proceedings of the 16th International Conference of Hong Kong Society for Transportation Studies. Hong Kong.

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Reviews

Amos O Olagunju

Effective social promotion networks require reliable algorithms for discovering events and targeting their locations to online users. But what are the underlying factors that affect ties among online social network (OSN) users The authors examine the relationship between user characteristics and their OSN connections. The user traits investigated in the research include user weight (number of friends), number of check-ins, lifespan, "the maximum amount of movements made by each user," and density (number of check-ins per day). The inside and outside fraction (IO-fraction)-"density outside the home is divided by density inside the home"-is used to gauge the association between distance, user activities, and user attributes. Experiments performed on data from three large-scale, location-based social networks examine the effects of user characteristics on OSN connections; the results are used to develop an algorithm for computing the likelihood of friendship materialization in OSNs. The IOF is used to exhibit the associations between mobility and user characteristics. Data analysis results reveal that the movement of users is relevant to their OSN activities; the probability of a pair of users forming a friendship within a distance can be rationally computed; and friendship probability and user weight, activity, lifespan, and movement can be estimated. The authors clearly recognize that the datasets used for the various research investigations do not contain "a time labeled friendship graph and location information in each time for every user." Consequently, the study fails to show the establishment of social connections. In the face of this limitation, I call on computational statisticians and online marketing research analysts to read this interesting paper and to help identify more reliable datasets and accurate models for advertising specific events to customers based on individual behavior.

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 13, Issue 3
June 2019
261 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3331063
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 May 2019
Accepted: 01 February 2019
Revised: 01 January 2019
Received: 01 January 2018
Published in TKDD Volume 13, Issue 3

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Author Tags

  1. Distance
  2. IO-fraction
  3. online social network
  4. tie formation
  5. user weight

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Cited By

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  • (2023)Community Detection Methods in Library’s Books and Borrowers Social Network SegmentationJournal of Advances in Information Technology10.12720/jait.14.6.1177-118514:6(1177-1185)Online publication date: 2023
  • (2023)A new link prediction method to alleviate the cold-start problem based on extending common neighbor and degree centralityPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.128546616(128546)Online publication date: Apr-2023
  • (2022)DEVELOPMENT OF A METHOD FOR CALCULATING THE PROTECTION OF PERSONAL DATA FROM THE CENTRALITY OF THE NETWORKCybersecurity: Education, Science, Technique10.28925/2663-4023.2022.15.35523:15(35-52)Online publication date: 2022
  • (2022)THE METHOD OF CALCULATION OF PERSONAL DATA PROTECTION ON THE BASIS OF A SET OF SPECIFIC PARAMETERS OF SOCIAL NETWORKSCollection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University10.17721/2519-481X/2022/76-05(54-68)Online publication date: 2022
  • (2022)Attribute augmented and weighted naive BayesScience China Information Sciences10.1007/s11432-020-3277-065:12Online publication date: 17-Nov-2022
  • (2021)Music Recommendation Algorithm Based on Knowledge graph Propagation User Preference2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)10.1109/ISIE45552.2021.9576346(1-6)Online publication date: 20-Jun-2021
  • (2021)Disentangled-based Adversarial Network for Multiplex Network Embedding2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534065(1-8)Online publication date: 2021
  • (2021)Cooperation Evolution in Multiplex Networks With the Heterogeneous Diffusion ModelIEEE Access10.1109/ACCESS.2021.30840599(86074-86082)Online publication date: 2021
  • (2020)Optimized Extreme Learning Clustering and Orthogonally Projected User Grouping for Online Social NetworksOptical Memory and Neural Networks10.3103/S1060992X2001008729:1(44-55)Online publication date: 2-Apr-2020
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