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Wang, X.; Zhang, M.; Fan, W.; Zhao, K.: Understanding the spread of COVID-19 misinformation on social media : the effects of topics and a political leader's nudge (2022)
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- Abstract
- The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.
- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.5, S.726-737
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Li, W.; Zheng, Y.; Zhan, Y.; Feng, R.; Zhang, T.; Fan, W.: Cross-modal retrieval with dual multi-angle self-attention (2021)
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- Abstract
- In recent years, cross-modal retrieval has been a popular research topic in both fields of computer vision and natural language processing. There is a huge semantic gap between different modalities on account of heterogeneous properties. How to establish the correlation among different modality data faces enormous challenges. In this work, we propose a novel end-to-end framework named Dual Multi-Angle Self-Attention (DMASA) for cross-modal retrieval. Multiple self-attention mechanisms are applied to extract fine-grained features for both images and texts from different angles. We then integrate coarse-grained and fine-grained features into a multimodal embedding space, in which the similarity degrees between images and texts can be directly compared. Moreover, we propose a special multistage training strategy, in which the preceding stage can provide a good initial value for the succeeding stage and make our framework work better. Very promising experimental results over the state-of-the-art methods can be achieved on three benchmark datasets of Flickr8k, Flickr30k, and MSCOCO.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.1, S.46-65
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Zhang, Y.; Li, X.; Fan, W.: User adoption of physician's replies in an online health community : an empirical study (2020)
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- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1179-1191
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Du, Q.; Li, J.; Du, Y.; Wang, G.A.; Fan, W.: Predicting crowdfunding project success based on backers' language preferences (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1558-1574
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Liu, Q.; Yang, Z.; Cai, X.; Du, Q.; Fan, W.: ¬The more, the better? : The effect of feedback and user's past successes on idea implementation in open innovation communities (2022)
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- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.3, S.376-392
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Liu, J.; Zhou, Z.; Gao, M.; Tang, J.; Fan, W.: Aspect sentiment mining of short bullet screen comments from online TV series (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.8, S.1026-1045
