A Social Network Analysis of Face Tracking in News Video

Renoust, Benjamin; Ngo, Thanh Duc; Le, Duy Dinh; Satoh, Shin’ichi

doi:10.1109/sitis.2015.30

Cited by 3 publications

(8 citation statements)

References 29 publications

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“…The effectiveness of the proposed algorithm has been tried to be shown by experiments on different databases of videos collected from YouTube. In a different study, it was proposed to use video analysis to automate the process of creating social networks directly from news videos 196 . The social networks are created from face detection and tracking of video content from the NHK News 7 broadcast and enriched with domain knowledge and segmentation.…”

Section: Semantic Analysis In Social Networkmentioning

confidence: 99%

“…In a different study, it was proposed to use video analysis to automate the process of creating social networks directly from news videos. 196 The social networks are created from face detection and tracking of video content from the NHK News 7 broadcast and enriched with domain knowledge and segmentation. The segmentation of the news by topic is based on semantic analysis.…”

Section: Semantic Video Analysismentioning

confidence: 99%

See 1 more Smart Citation

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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Section: Semantic Analysis In Social Networkmentioning

confidence: 99%

Section: Semantic Video Analysismentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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show abstract

“…The work reported in Ref. [12] utilized the technique of face recognition and built social networks of many public characters appeared in news in the last decade to assist understanding the Japanese media.…”

Section: Related Workmentioning

confidence: 99%

InSocialNet: Interactive visual analytics for role—event videos

Pan

Niu

et al. 2019

Comp. Visual Media

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Role-event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role-event videos. It automatically and dynamically constructs social networks from role-event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role-event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors.

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Section: Introductionmentioning

confidence: 99%

“…The interactions of stories elements have often been captured through the use of network modelling [12,7,1,5]. It has been used to support narration of a wide range of stories, from books [7], TV series [1], stories of news events [5], and movies [12], which make our focus. Networks are visuals objects that can not only be cleverly visualized to explicit the stories [5], but also their structure can be interrogated in matter of topology [7,20] These works are limited.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Network Model of Movie Script

Mourchid¹,

Renoust²,

Cherifi³

2018

Studies in Computational Intelligence

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Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration's content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights on movies. We demonstrate this model on two very popular movies.

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A Social Network Analysis of Face Tracking in News Video

Cited by 3 publications

References 29 publications

Semantic analysis on social networks: A survey

Semantic analysis on social networks: A survey

InSocialNet: Interactive visual analytics for role—event videos

Multilayer Network Model of Movie Script

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