scholarly journals Behavioral Habits-Based User Identification Across Social Networks

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1134 ◽  
Author(s):  
Xing ◽  
Deng ◽  
Wu ◽  
Xie ◽  
Gao
Keyword(s):  
Social Networks ◽  
User Behavior ◽  
Practical Significance ◽  
User Identification ◽  
Online Data ◽  
Matching Problems ◽  
Network Applications ◽  
Matching Process ◽  
User Data ◽  
The One

Social networking is an interactive Internet of Things. The symmetry of the network can reflect the similar friendships of users on different social networks. A user’s behavior habits are not easy to change, and users usually have the same or similar display names and published contents among multiple social networks. Therefore, the symmetry concept can be used to analyze the information generated by the user for user identification. User identification plays a key role in building better information about social network user profiles. As a consequence, it has very important practical significance in many network applications and has attracted a great deal of attention from researchers. However, existing works are primarily focused on rich network data and ignore the difficulty involved in data acquisition. Display names and user-published content are very easy to obtain compared to other types of user data across different social networks. Therefore, this paper proposes an across social networks user identification method based on user behavior habits (ANIUBH). We analyzed the user’s personalized naming habits in terms of display names, then utilized different similarity calculation methods to measure the similarity of the features contained in the display names. The variant entropy value was adopted to assign weights to the features mentioned above. In addition, we also measured and analyzed the user’s interest graph to further improve user identification performance. Finally, we combined one-to-one constraint with the Gale–Shapley algorithm to eliminate the one-to-many and many-to-many account-matching problems that often occur during the results-matching process. Experimental results demonstrated that our proposed method enables the possibility of user identification using only a small amount of online data.

scholarly journals A User Identification Algorithm Based on User Behavior Analysis in Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47114-47123 ◽  
Author(s):  
Kaikai Deng ◽  
Ling Xing ◽  
Longshui Zheng ◽  
Honghai Wu ◽  
Ping Xie ◽  
...  
Keyword(s):  
Social Networks ◽  
Behavior Analysis ◽  
User Behavior ◽  
Identification Algorithm ◽  
User Identification ◽  
User Behavior Analysis

scholarly journals Exploiting User Friendship Networks for User Identification across Social Networks

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 110
Author(s):  
Yating Qu ◽  
Ling Xing ◽  
Huahong Ma ◽  
Honghai Wu ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Recommendation System ◽  
User Behavior ◽  
Comprehensive Evaluation ◽  
Behavior Pattern ◽  
Recall Rate ◽  
Friendship Networks ◽  
User Identification ◽  
Identification Process ◽  
Gradient Descent Algorithm

Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user’s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale–Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.

scholarly journals Exploiting Two-Level Information Entropy across Social Networks for User Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ling Xing ◽  
Kaikai Deng ◽  
Honghai Wu ◽  
Ping Xie ◽  
Mingchuan Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Entropy ◽  
Online Social Networks ◽  
User Behavior ◽  
User Profile ◽  
Recall Rate ◽  
Entropy Method ◽  
Stable Marriage ◽  
User Identification ◽  
Level Information

As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).

scholarly journals User Identification Based on Integrating Multiple User Information across Online Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wenjing Zeng ◽  
Rui Tang ◽  
Haizhou Wang ◽  
Xingshu Chen ◽  
Wenxian Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
User Privacy ◽  
User Identification ◽  
Multiple User ◽  
Information User ◽  
User Data ◽  
The Impact

User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.

scholarly journals Commercial Limits to Personality: Instagram Influencers and Commoditized Content Receptivity

Societies ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 77
Author(s):  
Tyler Horan
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Identity ◽  
The Other ◽  
Audience Reception ◽  
Media Audiences ◽  
Unique Identity ◽  
The One ◽  
Commercial Relationships ◽  
Network Power

Social media influencers-individuals who utilize various forms of network power on social networks occupy a unique identity space. On the one hand, their network power is often tied to their social identity as creators of engaging material. On the other hand, their ability to promote commercial products and services steps outside the traditionally distinct commercial–social, occupational–personal divides. In this work, the network morphologies of influencers are explored in relation to their delivery of sponsored and non-sponsored content. This article explores how the disclosure of content as ‘sponsored’ affects audience reception. We show how that the promotion of content on social media often generates higher levels of engagement and receptiveness amongst their audience despite the platform’s assumption of organic non-commercial relationships. We find that engagement levels are highest among smaller out-degree networks. Additionally, we demonstrate that sponsored content not only returns a higher level of engagement, but that the effect of sponsorship is relatively consistent across out-degree network sizes. In sum, we suggest that social media audiences are not sensitive to commercial sponsorship when tied to identity, as long as that performance is convincing and consistent.

Enabling social networks in an R&D organization

2006 ◽  
Vol 25 (4) ◽  
pp. 237-246
Author(s):  
Tomas Hellström
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Qualitative Study ◽  
Network Formation ◽  
Higher Order ◽  
Practical Significance ◽  
Administrative Unit ◽  
High Level ◽  
Social Network Formation

This paper presents a qualitative study of mechanisms enabling social network formation in the R&D unit of a large technology-based organization. Drawing on interviews with 37 high-level technical and administrative unit members, a number of social network enablers could be discerned, which related to the need for effective location mechanisms, special “enrolment spaces”, and mechanisms for forging contacts. It was also possible to identify a number of higher-order factors for facilitation of network formation, namely hierarchical enablers and communicative and assimilative factors. Based on these results, the paper makes suggestions as to the theoretical and practical significance of social network enabling mechanisms in R&D organizations.

User identification across online social networks in practice: Pitfalls and solutions

2016 ◽  
Vol 44 (3) ◽  
pp. 377-391 ◽  
Author(s):  
Azadeh Esfandyari ◽  
Matteo Zignani ◽  
Sabrina Gaito ◽  
Gian Paolo Rossi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Full Range ◽  
Application Programming Interface ◽  
User Identification ◽  
Application Programming ◽  
Test Sets ◽  
Set Up ◽  
Different Levels ◽  
Programming Interface

To take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.

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