Flu Detector - Tracking Epidemics on Twitter

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.Multimed Tools Appl

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“…Other cases in the health domain have been studied for detecting the inception of public health seasonal flu [1,27,46], [7].…”

Section: Related Workmentioning

confidence: 99%

Predicting TV programme audience by using twitter based metrics

Crisci

Grasso

Nesi

et al. 2017

Multimed Tools Appl

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“…Previously, this system has been successfully used for several media analysis studies in both news and social media, ranging from predicting flu levels from Twitter content [17], analyzing public mood from social media [18], large-scale analysis of topic, style and gender bias in news content [19], and detecting patterns in the news coverage of US elections [20].…”

Section: Data Descriptionmentioning

confidence: 99%

On the coverage of science in the media: A big data study on the impact of the Fukushima disaster

Lansdall-Welfare

Sudhahar

Veltri

et al. 2014

2014 IEEE International Conference on Big Data (Big Data)

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Abstract-The contents of English-language online-news over 5 years have been analyzed to explore the impact of the Fukushima disaster on the media coverage of nuclear power. This big data study, based on millions of news articles, involves the extraction of narrative networks, association networks, and sentiment time series. The key finding is that media attitude towards nuclear power has significantly changed in the wake of the Fukushima disaster, in terms of sentiment and in terms of framing, showing a long lasting effect that does not appear to recover before the end of the period covered by this study. In particular, we find that the media discourse has shifted from one of public debate about nuclear power as a viable option for energy supply needs to a re-emergence of the public views of nuclear power and the risks associated with it. The methodology used presents an opportunity to leverage big data for corpus analysis and opens up new possibilities in social scientific research.

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“…There has been extensive research for detecting Twitter events like epidemics (Lampos et al, 2010), wildfires, hurricanes, floods (Starbird et al, 2010), earthquakes (Sakaki et al, 2010) and tornados. One can use Twitter Trends words, Latent Dirichlet Allocation (LDA) (Blei et al, 2003) or Phrase Graph Generation Method (Sharifi et al, 2010) for detecting events.…”

Section: Event Detectionmentioning

confidence: 99%

Predicting future popularity trend of events in microblogging platforms

Gupta¹,

Gao

Zhai³

et al. 2012

Proc of Assoc for Info

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The fast information sharing on Twitter from millions of users all over the world leads to almost real-time reporting of events. It is extremely important for business and administrative decision makers to learn events' popularity as quickly as possible, as it can buy extra precious time for them to make informed decisions. Therefore, we introduce the problem of predicting future popularity trend of events on microblogging platforms. Traditionally, trend prediction has been performed by using time series analysis of past popularity to forecast the future popularity changes. As we can encode the rich Twitter dynamics using a rich variety of features from microblogging data, we explore regression, classification and hybrid approaches, using a large set of popularity, social and event features, to predict event popularity. Experimental results on two real datasets of 18382 events extracted from ~133 million tweets show the effectiveness of the extracted features and learning approaches. The predicted popularity trend of events can be directly used for a variety of applications including recommendation systems, ad keywords bidding price decisions, stock trading decisions, dynamic ticket pricing for sports events, etc.

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Flu Detector - Tracking Epidemics on Twitter

Cited by 189 publications

References 9 publications

Predicting TV programme audience by using twitter based metrics

Predicting TV programme audience by using twitter based metrics

On the coverage of science in the media: A big data study on the impact of the Fukushima disaster

Predicting future popularity trend of events in microblogging platforms

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