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Large-scale data from social media have a significant potential to describecomplex phenomena in real world and to anticipate collective behaviors such asinformation spreading and social trends. One specific case of study isrepresented by the collective attention to the action of political parties. Notsurprisingly, researchers and stakeholders tried to correlate parties' presenceon social media with their performances in elections. Despite the many efforts,results are still inconclusive since this kind of data is often very noisy andsignificant signals could be covered by (largely unknown) statisticalfluctuations. In this paper we consider the number of tweets (tweet volume) ofa party as a proxy of collective attention to the party, identify the dynamicsof the volume, and show that this quantity has some information on theelections outcome. We find that the distribution of the tweet volume for eachparty follows a log-normal distribution with a positive autocorrelation of thevolume over short terms, which indicates the volume has large fluctuations ofthe log-normal distribution yet with a short-term tendency. Furthermore, bymeasuring the ratio of two consecutive daily tweet volumes, we find that theevolution of the daily volume of a party can be described by means of ageometric Brownian motion (i.e., the logarithm of the volume moves randomlywith a trend). Finally, we determine the optimal period of averaging tweetvolume for reducing fluctuations and extracting short-term tendencies. Weconclude that the tweet volume is a good indicator of parties' success in theelections when considered over an optimal time window. Our study identifies thestatistical nature of collective attention to political issues and sheds lighton how to model the dynamics of collective attention in social media.

Twitter-based analysis of the dynamics of collective attention to political parties