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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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Related in: MedlinePlus

Heatmap of geotagged Twitter activity.Twitter activity related to the Occupy Wall-Street (OWS) Movement, collected for hashtags, or topics, used by protests or members of the movement. The “redder” areas indicate regions with more tweets. Here we see two extremes of geotagging behavior. Panel (a) shows the tweets for 15 November 2011, when the New York Police Department attempted to remove protesters from Zuccotti Park. Panel (b) shows the tweets for 26 December 2011, when protesting had dwindled. In between these two extremes of activity, is a more general pattern of discussion centered around the protests in Zuccotti Park.
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pone-0102001-g001: Heatmap of geotagged Twitter activity.Twitter activity related to the Occupy Wall-Street (OWS) Movement, collected for hashtags, or topics, used by protests or members of the movement. The “redder” areas indicate regions with more tweets. Here we see two extremes of geotagging behavior. Panel (a) shows the tweets for 15 November 2011, when the New York Police Department attempted to remove protesters from Zuccotti Park. Panel (b) shows the tweets for 26 December 2011, when protesting had dwindled. In between these two extremes of activity, is a more general pattern of discussion centered around the protests in Zuccotti Park.

Mentions: Many of the tweets collected were geotagged, with a large number of the geotagged tweets coming from New York City. Figure 1 shows a heatmap of the tweets produced on different days and we can see the extreme cases of geotagged tweets. Figure 1(a) shows the tweets for 15 November 2011, when the New York Police Department attempted to remove protesters from Zuccotti Park. Figure 1(b) shows the tweets for 26 December 2011, when protesting had dwindled. In between these two extremes of activity, is a more general pattern of discussion centered around the protests in Zuccotti Park.


Discovering social events through online attention.

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

Heatmap of geotagged Twitter activity.Twitter activity related to the Occupy Wall-Street (OWS) Movement, collected for hashtags, or topics, used by protests or members of the movement. The “redder” areas indicate regions with more tweets. Here we see two extremes of geotagging behavior. Panel (a) shows the tweets for 15 November 2011, when the New York Police Department attempted to remove protesters from Zuccotti Park. Panel (b) shows the tweets for 26 December 2011, when protesting had dwindled. In between these two extremes of activity, is a more general pattern of discussion centered around the protests in Zuccotti Park.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0102001-g001: Heatmap of geotagged Twitter activity.Twitter activity related to the Occupy Wall-Street (OWS) Movement, collected for hashtags, or topics, used by protests or members of the movement. The “redder” areas indicate regions with more tweets. Here we see two extremes of geotagging behavior. Panel (a) shows the tweets for 15 November 2011, when the New York Police Department attempted to remove protesters from Zuccotti Park. Panel (b) shows the tweets for 26 December 2011, when protesting had dwindled. In between these two extremes of activity, is a more general pattern of discussion centered around the protests in Zuccotti Park.
Mentions: Many of the tweets collected were geotagged, with a large number of the geotagged tweets coming from New York City. Figure 1 shows a heatmap of the tweets produced on different days and we can see the extreme cases of geotagged tweets. Figure 1(a) shows the tweets for 15 November 2011, when the New York Police Department attempted to remove protesters from Zuccotti Park. Figure 1(b) shows the tweets for 26 December 2011, when protesting had dwindled. In between these two extremes of activity, is a more general pattern of discussion centered around the protests in Zuccotti Park.

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