Rumour Veracity Estimation with Deep Learning for Twitter
IFIP Advances in Information and Communication Technology, ISSN: 1868-422X, Vol: 558, Page: 351-363
2019
- 11Citations
- 15Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85068483804&origin=inward; http://dx.doi.org/10.1007/978-3-030-20671-0_24; https://link.springer.com/10.1007/978-3-030-20671-0_24; https://dx.doi.org/10.1007/978-3-030-20671-0_24; https://link.springer.com/chapter/10.1007/978-3-030-20671-0_24
Springer Science and Business Media LLC
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