A deep dive into user display names across social networks

Li, Yongjun; Peng, You; Zhang, Zhen; Ming-jie, WU; Xu, Quanqing; Yin, Hongzhi

doi:10.1016/j.ins.2018.02.072

Cited by 27 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When users register social network accounts, most users will utilize these three features to combine the display name. Li et al [30] concluded that more than 45% of users have the same display name on different social networks, which provides an effective basis for the work of this paper. Accordingly, we used different similarity calculation methods to measure and analyzed the above three features.…”

Section: Display Name Analysismentioning

confidence: 92%

Behavioral Habits-Based User Identification Across Social Networks

Xing

Deng

et al. 2019

Symmetry

View full text Add to dashboard Cite

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.

show abstract

Section: Display Name Analysismentioning

confidence: 92%

Behavioral Habits-Based User Identification Across Social Networks

Xing

Deng

et al. 2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…Li et al [190] 10. user's structural and content information modeling the topics of user's interests from contents, capturing the interest-based and friend-based user cooccurrence in a G to compromise user's privacy.…”

Section: B De-anonymization Methods Employed By the Adversaries To Jmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

View full text Add to dashboard Cite

“…After combining the mixed weight, the probability model can be converted into (10). Here, we use a logarithmic formula to avoid the underflow error.…”

Section: P(l|t) ∝ P(l) ×mentioning

confidence: 99%

“…Users can enter their location information manually or automatically via devices. However, for reasons related to protecting personal privacy and security, users are usually reluctant to share their real information [2], [10]. Many users turn off the device's geolocalization function or use forged location information.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Geolocalization of User-Generated Short Text Based on a Weight Probability Model

Gao

Yang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Recently, the fine-grained geolocalization of User-Generated Short Texts (UGST) has become increasingly important. One challenge is that UGST contains relatively little location-indicative information due to such limitations as text length. Therefore, extract and effectively use the location-indicative information is the key issue for improving the effect of geolocalization. The existing works only consider the global weight of the terms and do not distinguish between the importance of identical terms in different locations. In addition, the existing add-one smoothing masks the difference between the features of different locations. In this paper, we propose a fine-grained geolocalization method to predict the PoI-level location of UGSTs based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location-indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of nongeotagged UGST posted from each location and selecting k locations according to the top-k probabilities. The accuracy of FGST-WP on the three groundtruth datasets reaches 45%, 68%, and 72%, respectively. The results indicate the superior performance of FGST-WP.INDEX TERMS Geolocalization, weight probability model, user-generated short text.

show abstract

A deep dive into user display names across social networks

Cited by 27 publications

References 15 publications

Behavioral Habits-Based User Identification Across Social Networks

Behavioral Habits-Based User Identification Across Social Networks

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Fine-Grained Geolocalization of User-Generated Short Text Based on a Weight Probability Model

Contact Info

Product

Resources

About