SociRank : 基於社群媒體影響力之新聞重要性排序

SociRank : Ranking prevalent topics using social media factors

戴瑞克(Derek Davis)

指導教授 ： 陳宜欣

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2014年)

摘要

Historically, information which apprises us of daily events has been provided by mass media sources, specifically news media. Presently social media services, such as Twitter, provide an enormous amount of user generated data which has great potential to contain informative news related content. However, for this content to be useful we must find a way to filter noise and capture only such information that, based on its content similarity to news media, may potentially be considered useful or valuable. However, even after noise is removed there still exists a problem of information overload in the remaining data. A person is incapable of processing huge amounts of information all at once and thus information which is of most value must be prioritized for consumption. To achieve prioritization, the information must be ranked in order of estimated importance. The temporal prevalence of a particular topic in news media is one significant factor of importance and may be considered the media focus of a topic. The topic’s temporal prevalence in social media, specifically Twitter, indicates user interest and may be considered its user attention. Furthermore, the interaction between the social media users whom mention this topic indicates the strength of the community discussing said topic and may be considered the user interaction. We propose an unsupervised method called SociRank, which identifies news topics that are prevalent in both social and news media and then ranks these topics taking into account media focus, user attention and user interaction as measures of importance.

關鍵字

資訊過濾 ； 主題偵測 ； 主題排序 ； 社群網路分析

並列摘要

Historically, information which apprises us of daily events has been provided by mass media sources, specifically news media. Presently social media services, such as Twitter, provide an enormous amount of user generated data which has great potential to contain informative news related content. However, for this content to be useful we must find a way to filter noise and capture only such information that, based on its content similarity to news media, may potentially be considered useful or valuable. However, even after noise is removed there still exists a problem of information overload in the remaining data. A person is incapable of processing huge amounts of information all at once and thus information which is of most value must be prioritized for consumption. To achieve prioritization, the information must be ranked in order of estimated importance. The temporal prevalence of a particular topic in news media is one significant factor of importance and may be considered the media focus of a topic. The topic’s temporal prevalence in social media, specifically Twitter, indicates user interest and may be considered its user attention. Furthermore, the interaction between the social media users whom mention this topic indicates the strength of the community discussing said topic and may be considered the user interaction. We propose an unsupervised method called SociRank, which identifies news topics that are prevalent in both social and news media and then ranks these topics taking into account media focus, user attention and user interaction as measures of importance.

並列關鍵字

information filtering ； topic identification ； topic ranking ； social network analysis

參考文獻

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