Detecting fake news over online social media via domain reputations and content understanding

Xu, Kuai; Wang, Rui; Wang, Haiyan; Yang, Bo

doi:10.26599/tst.2018.9010139

Cited by 76 publications

(35 citation statements)

References 17 publications

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“…The authors in [27] utilize people's ever-accessed content to measure the similarity between different people. In other words, if two individuals have accessed the same or similar contents, then they can be approximately deemed as similar people.…”

Section: A Similarity-based Matchingmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

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Section: A Similarity-based Matchingmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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“…There is a lack in utilizing semantic features in fake news detection, only few studies have utilized these features in their works [10]. The results of those works have showed that using semantic features to detect fake news is very important and effective and need more investigation [11]. Sentiment features analysis is an important approach to detect fake news and expose the suspicious and fake user accounts [12].…”

Section: Content-based Features Analysismentioning

confidence: 99%

Content-Social Based Features for Fake News Detection Model from Twitter

Hussein¹

2019

IJATCSE

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The advancement of social networks has facilitated the sharing and spread of news among people over the world. With the growth of these networks and growth of the volume of the news shared daily, the phenomena of fake news become more stronger and widely spread. In this paper, content-social based features for fake news detection model from Twitter data has been proposed. This work aims to analyze content-based features of news content including of linguistic features, writing style features, semantic features and sentiment features along with social-context based features of news diffusion over the social network including user-based features and network-based features to detect fake news from Twitter news posts. With using of unsupervised graph-based clustering approach, no labelled data is required and this make the proposed model more practical to detect online fake news.

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“…[24], an attacker uses user profiles and social relationships collectively to predict sensitive information of related victims in a released social network dataset. Reference [25] proposed an approach for detecting fake news over online social media via domain reputations and content understanding. The authors of Ref.…”

Section: Introductionmentioning

confidence: 99%

Effective algorithms to detect stepping-stone intrusion by removing outliers of packet RTTs

Wang

Yang

Workman

et al. 2022

Tsinghua Sci. Technol.

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An effective method to detect stepping-stone intrusion (SSI) is to estimate the length of a connection chain. This type of detection method is referred to as a network-based detection approach. Existing network-based SSI detection methods are either ineffective in the context of the Internet because of the presence of outliers in the packet round-trip times (RTTs) or inefficient, as many packets must be captured and processed. Because of the high fluctuation caused by the intermediate routers on the Internet, it is unavoidable that the RTTs of the captured packets contain outlier values. In this paper, we first propose an efficient algorithm to eliminate most of the possible RTT outliers of the packets captured in the Internet environment. We then develop an efficient SSI detection algorithm by mining network traffic using an improved version of k-Means clustering. Our proposed detection algorithm for SSI is accurate, effective, and efficient in the context of the Internet. Well-designed network experiments are conducted in the Internet environment to verify the effectiveness, correctness, and efficiency of our proposed algorithms. Our experiments show that the effective rate of our proposed SSI detection algorithm is higher than 85.7% in the context of the Internet.

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Detecting fake news over online social media via domain reputations and content understanding

Cited by 76 publications

References 17 publications

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Content-Social Based Features for Fake News Detection Model from Twitter

Effective algorithms to detect stepping-stone intrusion by removing outliers of packet RTTs

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