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    <titleInfo>
        <title>Content-based Influence Modeling for Opinion Behavior Prediction</title>
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        <namePart type="given">Chengyao</namePart>
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        <namePart type="given">Zhitao</namePart>
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    <name type="personal">
        <namePart type="given">Wenjie</namePart>
        <namePart type="family">Li</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Nowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person’s opinion about a product is influenced by his friends. Though various approaches have been proposed to study the opinion formation problem, they all formulate opinions as the derived sentiment values either discrete or continuous without considering the semantic information. In this paper, we propose a Content-based Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users’ future opinions. The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors. In the experiments conducted on Twitter datasets, our model significantly outperforms other popular opinion formation models.</abstract>
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        <url>https://aclanthology.org/C16-1208</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T Content-based Influence Modeling for Opinion Behavior Prediction
%A Chen, Chengyao
%A Wang, Zhitao
%A Lei, Yu
%A Li, Wenjie
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chen-etal-2016-content
%X Nowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person’s opinion about a product is influenced by his friends. Though various approaches have been proposed to study the opinion formation problem, they all formulate opinions as the derived sentiment values either discrete or continuous without considering the semantic information. In this paper, we propose a Content-based Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users’ future opinions. The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors. In the experiments conducted on Twitter datasets, our model significantly outperforms other popular opinion formation models.
%U https://aclanthology.org/C16-1208
%P 2207-2216

Markdown (Informal)

[Content-based Influence Modeling for Opinion Behavior Prediction](https://aclanthology.org/C16-1208) (Chen et al., COLING 2016)

• Content-based Influence Modeling for Opinion Behavior Prediction (Chen et al., COLING 2016)

ACL

• Chengyao Chen, Zhitao Wang, Yu Lei, and Wenjie Li. 2016. Content-based Influence Modeling for Opinion Behavior Prediction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2207–2216, Osaka, Japan. The COLING 2016 Organizing Committee.