Celebrity profiling through linguistic analysis of digital social networks

Q1 Mathematics

Computational Social Networks Pub Date : 2021-08-26 DOI:10.1186/s40649-021-00097-w

Moreno-Sandoval, Luis G., Pomares-Quimbaya, Alexandra, Alvarado-Valencia, Jorge A.

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引用次数: 3

Abstract

Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.

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通过数字社交网络的语言分析来分析名人

数字社交网络已经成为一个重要的信息来源，因为名人用它来分享他们的观点、想法、想法和感受。这使得数字社交网络成为名人宣传自己和吸引新粉丝的首选手段之一。本文提出了一种基于名人使用数字社交网络Twitter的特征选择模型。该模型包括对名人微博的词汇、句法、符号、参与和互补信息等特征进行分析，以此估算名人微博的人口学特征和影响力特征。具有这些新特征的分类在名气方面得分为0.65，性别方面得分为0.88，出生年份方面得分为0.37，职业方面得分为0.57。有了这些新功能，平均精度提高了0.14以上。结果表明，从语言线索中提取的特征提高了名声和性别预测模型的性能，并有助于对模型结果的解释。特别是，第三人称单数的使用在Fame模型中具有很高的预测性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.