2021
DOI: 10.1007/s13278-021-00819-y
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Rumour prevention in social networks with layer 2 blockchains

Abstract: Social bots can cause social, political, and economical disruptions by spreading rumours. The state-of-the-art methods to prevent social bots from spreading rumours are centralised and such solutions may not be accepted by users who may not trust a centralised solution being biased. In this paper, we developed a decentralised method to prevent social bots. In this solution, the users of a social network create a secure and privacy-preserving decentralised social network and may accept social media content if i… Show more

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Cited by 4 publications
(3 citation statements)
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References 31 publications
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“…Proposed Model [46] Blockchain and keyed watermarking-based framework [47] Architecture of blockchain-based fictitious detection system [48] Architecture to store validation records in the blockchain [49] Rating system to detect the authenticity of news [50] A secure and privacy-preserving method [51] A framework for safely sharing information at the peer level [52] Machine Learning-based model using Blockchain [56] AI with a Proof-of-Stake smart contract algorithm architecture [57] Blockchain with Text Mining (TM) algorithm [58] Social media news-spreading model [61] A blockchain and watermarking-based social media framework [63] TRUSTD, a blockchain and collective signature-based ecosystem [64] A smart contract-based technique to avert fake posts [66] WhistleBlower, a decentralized fake news detection platform [67] Proof of Trustworthiness (PoT) [69] Avoiding false check-ins in Location-Based Social Networks [72] Method using the concept of decentralization [73] A blockchain-based social media notarization method [74] A blockchain consensus called Proof of Credibility (PoC) [75] A public opinion communication model [77] Architecture using data mining as a consensus algorithm [78] A prototype to counter the dissemination of fake news [79] Proof of Truthfulness (PoT) for news verification [85] Provenator, a blockchain-based distributed application…”
Section: Referencementioning
confidence: 99%
See 1 more Smart Citation
“…Proposed Model [46] Blockchain and keyed watermarking-based framework [47] Architecture of blockchain-based fictitious detection system [48] Architecture to store validation records in the blockchain [49] Rating system to detect the authenticity of news [50] A secure and privacy-preserving method [51] A framework for safely sharing information at the peer level [52] Machine Learning-based model using Blockchain [56] AI with a Proof-of-Stake smart contract algorithm architecture [57] Blockchain with Text Mining (TM) algorithm [58] Social media news-spreading model [61] A blockchain and watermarking-based social media framework [63] TRUSTD, a blockchain and collective signature-based ecosystem [64] A smart contract-based technique to avert fake posts [66] WhistleBlower, a decentralized fake news detection platform [67] Proof of Trustworthiness (PoT) [69] Avoiding false check-ins in Location-Based Social Networks [72] Method using the concept of decentralization [73] A blockchain-based social media notarization method [74] A blockchain consensus called Proof of Credibility (PoC) [75] A public opinion communication model [77] Architecture using data mining as a consensus algorithm [78] A prototype to counter the dissemination of fake news [79] Proof of Truthfulness (PoT) for news verification [85] Provenator, a blockchain-based distributed application…”
Section: Referencementioning
confidence: 99%
“…In addition, it cannot be used with social media messaging platforms due to its considerable operation ratio. Subhasis Thakur et al [50] designed a decentralized structure to avoid social bots by deploying a public PoW-based blockchain. Users can create a decentralized architecture, providing them with a safe environment and protecting their privacy.…”
Section: Referencementioning
confidence: 99%
“…This manipulation has led to biased information flow, favouring specific perspectives, products, or organizations, hindering users' access to accurate and diverse information, ultimately disrupting the human ecology (Volkova, Lazutova, 2017). In recent years, researchers have dedicated a significant amount of attention to social media bot detection (Aljabri et al, 2023;Ferrara, 2018;Shao et al, 2018) and prevention (Kavazi et al, 2021;Thakur, Breslin, 2021), and defense.…”
mentioning
confidence: 99%