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- W4375817816 abstract "Developing technologies and new social media platforms (Facebook, YouTube, Flickr, and Twitter) are transforming how people communicate with each other and scrutinize information. Different social media users prefer different mode of information for expressing their views. For instance, Twitter users prefer short text and informally taken photos; YouTube viewers tend to discuss breaking news in the form of short videos with rough editing, and professional journalists usually produce standardized news stories with substantial text and well chosen images. Every day, a large number of videos, images, and text are shared on social media platforms. The cross-media data provide credible insights into public opinion, which are extremely beneficial to commercial firms, governments, and any organization concerned with societal opinions. As a result, assessing such voluminous data is beneficial. When cross-media information is combined, it can provide a more comprehensive and effective description of a given event or issue, as well as improve system performance. Also, in addition to being a significant source of information, cross-media information provided by various media is more robust, has a larger audience, and appears to be a more accurate reflection of real-world events. Consequently, robustly identifying topics from multimodal data from multiple media is a useful and sensible extension of cross-media topic identification. This paper presents a review of various cross-media topic detection approaches." @default.
- W4375817816 created "2023-05-09" @default.
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- W4375817816 date "2023-01-01" @default.
- W4375817816 modified "2023-09-26" @default.
- W4375817816 title "Cross-Media Topic Detection: Approaches, Challenges, and Applications" @default.
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- W4375817816 doi "https://doi.org/10.1007/978-981-19-7867-8_45" @default.
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