Research Area: Blockchain Technology
In the era of social networks, the scale and speed of online information dissemination have been greatly enhanced, thus leading to a large number of meme being generated, spread and popularized in the Internet. Social networks strongly promote the replication and dissemination of modalities, which are powerfully explosive and can be copied and spread in large numbers in a short period of time. Because of the typical decentralized nature of meme propagation in social networks, the development of blockchain technology provides a reliable environment for meme propagation models. However, there are various hurdles to using online user behavior data to predict meme popularity, such as the fact that there are only a few user comments and retweets beneath a meme topic, making it difficult to predict meme popularity directly by basic social data mining at this moment. In this scenario, mining the contextual information of web users can considerably increase meme prediction performance. Because social data contains a wealth of contextual and user relationship characteristics, we offer for the first time a blockchain-assisted based meme popularity prediction (BMP) mechanism based on empirical approach. To begin, we suggest a blockchain-based data storage approach to mimic a decentralized ecosystem. Following that, we assess meme popularity in terms of four contextually based web user characteristics. Finally, using a probabilistic linear model, we present a meme popularity prediction model that integrates the four contextual characteristics. By making a comparison of comprehensive tests with existing methodologies, we illustrate the usefulness and accuracy of our proposed model. The experimental results indicate that the proposed meme prediction approach can provide a meme prediction service with high accuracy, a well-defined decentralized environment, and steady performance, making it a reliable service for recommendation systems and web-based information dissemination.
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Author(s) Name: Fan Yang, Yanan Qiao, Yanan Qiao, Junge Bo, Xiao Wang
Journal name: Information Sciences
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Publisher name: Elsevier
DOI: 10.1016/j.ins.2022.04.039
Volume Information: Volume 603, July 2022, Pages 262-288
