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Discovering social events through online attention.

Kenett DY, Morstatter F, Stanley HE, Liu H - PLoS ONE (2014)

Bottom Line: We compare methods commonly found in the literature with a method from economics.By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter.We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

View Article: PubMed Central - PubMed

Affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, United States of America.

ABSTRACT
Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

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Comparison of the HHI to its underlying parameters: the number of tweets, and number of hashtags.Here, the diagonal figures represent the histogram of values for each of these three parameters, whereas the off diagonal panels represent a comparison of the values of two different parameters. It is clear by studying these figures that the HHI is not merely a function of either the number of tweets or number of users.
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pone-0102001-g003: Comparison of the HHI to its underlying parameters: the number of tweets, and number of hashtags.Here, the diagonal figures represent the histogram of values for each of these three parameters, whereas the off diagonal panels represent a comparison of the values of two different parameters. It is clear by studying these figures that the HHI is not merely a function of either the number of tweets or number of users.

Mentions: We use HHI analysis to study the OWS dataset and calculate the HHI value for a time horizon of a single day, using the number of users and hashtags. One concern of the HHI is that it is dependent on the number of tweets produced in a given time interval. Figure 2 shows the time evolution of the HHI. Figure 3 compares the HHI with its underlying parameters: the number of users and the number of hashtags. Here the diagonal figures represent the histogram of values for each of these three parameters, whereas the off-diagonal panels represent a comparison of the values of two different parameters. Studying this figure, it is clear that the HHI is not merely a function of either of these two parameters.


Discovering social events through online attention.

Kenett DY, Morstatter F, Stanley HE, Liu H - PLoS ONE (2014)

Comparison of the HHI to its underlying parameters: the number of tweets, and number of hashtags.Here, the diagonal figures represent the histogram of values for each of these three parameters, whereas the off diagonal panels represent a comparison of the values of two different parameters. It is clear by studying these figures that the HHI is not merely a function of either the number of tweets or number of users.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4116114&req=5

pone-0102001-g003: Comparison of the HHI to its underlying parameters: the number of tweets, and number of hashtags.Here, the diagonal figures represent the histogram of values for each of these three parameters, whereas the off diagonal panels represent a comparison of the values of two different parameters. It is clear by studying these figures that the HHI is not merely a function of either the number of tweets or number of users.
Mentions: We use HHI analysis to study the OWS dataset and calculate the HHI value for a time horizon of a single day, using the number of users and hashtags. One concern of the HHI is that it is dependent on the number of tweets produced in a given time interval. Figure 2 shows the time evolution of the HHI. Figure 3 compares the HHI with its underlying parameters: the number of users and the number of hashtags. Here the diagonal figures represent the histogram of values for each of these three parameters, whereas the off-diagonal panels represent a comparison of the values of two different parameters. Studying this figure, it is clear that the HHI is not merely a function of either of these two parameters.

Bottom Line: We compare methods commonly found in the literature with a method from economics.By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter.We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

View Article: PubMed Central - PubMed

Affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, United States of America.

ABSTRACT
Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

Show MeSH
Related in: MedlinePlus