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Predicting TV programme audience by using twitter based metrics

机译:通过使用基于Twitter的指标来预测电视节目的观众

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摘要

The predictive capabilities of metrics based on Twitter data have been stressed in different fields: business, health, market, politics, etc. In specific cases, a deeper analysis is required to create useful metrics and models with predicting capabilities. In this paper, a set of metrics based on Twitter data have been identified and presented in order to predict the audience of scheduled television programmes, where the audience is highly involved such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy). Identified suitable metrics are based on the volume of tweets, the distribution of linguistic elements, the volume of distinct users involved in tweeting, and the sentiment analysis of tweets. On this ground a number of predictive models have been identified and compared. The resulting method has been selected in the context of a validation and assessment by using real data, with the aim of building a flexible framework able to exploit the predicting capabilities of social media data. Further details are reported about the method adopted to build models which focus on the identification of predictors by their statistical significance. Experiments have been based on the collected Twitter data by using Twitter Vigilance platform, which is presented in this paper, as well.
机译:基于Twitter数据的指标的预测功能已在不同领域中得到强调:商业,卫生,市场,政治等。在特定情况下,需要进行更深入的分析,以创建具有预测功能的有用指标和模型。在本文中,已经确定并提出了一组基于Twitter数据的指标,以便预测预定电视节目的受众,在这些受众中,诸如现场表演(例如X Factor和Pechino Express,在意大利)。确定的合适指标基于推文的数量,语言元素的分布,推文中涉及的不同用户的数量以及推文的情感分析。在此基础上,已经确定并比较了许多预测模型。通过使用真实数据在验证和评估的背景下选择了生成的方法,目的是建立一个能够利用社交媒体数据的预测功能的灵活框架。报告了有关构建模型的方法的更多详细信息,该模型着重于通过预测变量的统计意义来识别预测变量。本文还介绍了使用Twitter Vigilance平台基于收集到的Twitter数据进行的实验。

著录项

  • 来源
    《Multimedia Tools and Applications》 |2018年第10期|12203-12232|共30页
  • 作者单位

    CNR IBIMET National Research Council;

    CNR IBIMET National Research Council,LAMMA Consortium, Tuscany Region-CNR;

    DISIT Lab, Distributed [Systems and internet | Data Intelligence and] Technologies Lab, Department of Information Engineering (DINFO), University of Florence;

    DISIT Lab, Distributed [Systems and internet | Data Intelligence and] Technologies Lab, Department of Information Engineering (DINFO), University of Florence;

    DISIT Lab, Distributed [Systems and internet | Data Intelligence and] Technologies Lab, Department of Information Engineering (DINFO), University of Florence;

    DISIT Lab, Distributed [Systems and internet | Data Intelligence and] Technologies Lab, Department of Information Engineering (DINFO), University of Florence;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Twitter monitoring; Social media monitoring; Predicting audience; Twitter data analysis;

    机译:Twitter监视;社交媒体监视;预测受众;Twitter数据分析;

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