Hwe

Song, Xuebo; Srimani, Pradip K.; Wang, James

doi:10.1145/3342827.3342837

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…To resolve this problem, word embedding language models (Mikolov et al (2013), Pennington et a. (2014), Song et al (2019) ) come into picture that capture semantics or meaningful relationships. So that, we calculate the semantic similarity between tweets using Word Mover's Distance (WMD) (Kusner et al (2015)) based on Word2Vec model (Mikolov et al (2013)).…”

Section: Diversitymentioning

confidence: 99%

Semrank

Singh

2021

International Journal of Cognitive Informatics and Natural Intelligence

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Popular real-world events often create huge traffic on Twitter including real-time updates of important moments, personal comments, and so on while the event is happening. Most of the users are interested to read the important tweets that possibly include important moments of that event. However, extracting the relevant tweets of any event is a challenging task due to the endless stream of noisy tweets and vocabulary variation problem of social media content. To handle these challenges, the authors introduce a new approach for computing the relative tweet importance based on the concept of the Pagerank algorithm where adjacency matrix of the graph representation of tweets contains semantic similarity matrix based on the word mover's distance measure utilizing Word2Vec word embedding model. The results show that top-ranked tweets generated by the proposed approach are more concise and news-worthy than baseline approaches.

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