Quantifying Collective Attention from Tweet Stream
Publication Date
April 30, 2013
Journal
PLOS ONE
Authors
Kazutoshi Sasahara, Yoshito Hirata, Masashi Toyoda, Masaru Kitsuregawa, et al
Volume
8
Issue
4
Pages
e61823
DOI
https://dx.plos.org/10.1371/journal.pone.0061823
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0061823
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23637913
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640043
Europe PMC
http://europepmc.org/abstract/MED/23637913
Web of Science
000319077300016
Scopus
84876955183
Mendeley
http://www.mendeley.com/research/quantifying-collective-attention-tweet-stream
Events
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Mendeley | Further Information

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Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of \"collective attention\" on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. 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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1048937"], "description"=>"<p>(A) Tohoku-oki earthquake ( of nodes = 27,340, of links = 27,709), (B) FIFA Women's World Cup 2011 ( of nodes = 9,277, of links = 8,450), (C) Castle in the Sky ( of nodes = 1,183, of links = 793), and (D) total lunar eclipse ( of nodes = 893, of links = 553). The nodes represent users, which are connected if there is a RT with key terms related to the target event. In each figure, the node sizes are proportional to the number of retweeted tweets. Only the nodes with retweeted tweets are shown for clarity.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "rt", "networks"], "article_id"=>694208, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g006", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visualization_of_RT_networks_during_collective_attention_/694208", "title"=>"Visualization of RT networks during collective attention.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:10:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048940", "https://ndownloader.figshare.com/files/1048942", "https://ndownloader.figshare.com/files/1048943", "https://ndownloader.figshare.com/files/1048945", "https://ndownloader.figshare.com/files/1048946"], "description"=>"<div><p></p><p>Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.</p> </div>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "tweet"], "article_id"=>694211, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0061823.s001", "https://dx.doi.org/10.1371/journal.pone.0061823.s002", "https://dx.doi.org/10.1371/journal.pone.0061823.s003", "https://dx.doi.org/10.1371/journal.pone.0061823.s004", "https://dx.doi.org/10.1371/journal.pone.0061823.s005"], "stats"=>{"downloads"=>12, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantifying_Collective_Attention_from_Tweet_Stream_/694211", "title"=>"Quantifying Collective Attention from Tweet Stream", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-04-30 01:10:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048925"], "description"=>"<p>(A) Tweet count series for February and March 2011. Unthresholded recurrence plot <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061823#pone.0061823-Iwanski1\" target=\"_blank\">[41]</a> of the tweet time series (B) before the Tohoku-oki earthquake (March 4–10, 2011) and (C) after the earthquake (March 11–17, 2011). The gray scale corresponds to the distance between the tweet count series in a reconstructed phase space. Darker points indicate that two corresponding states are close to each other, whereas lighter points indicate that two corresponding states are distant from each other.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "irregular", "states", "tweet"], "article_id"=>694196, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regular_and_irregular_states_of_the_tweet_stream_/694196", "title"=>"Regular and irregular states of the tweet stream.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:09:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048939"], "description"=>"<p>Smaller values of and were correlated with the detection of more collective attention candidates. However, many false positive candidates were detected when these parameters were very small.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "dependence", "detection"], "article_id"=>694210, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_dependence_during_the_detection_of_collective_attention_candidates_/694210", "title"=>"Parameter dependence during the detection of collective attention candidates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:10:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048933"], "description"=>"<p>Tweet probability distribution, and the rankings of the popularity and the enhancement of key terms in Japanese tweets on (A) December 9, 2011 and (B) December 10, 2011. The collective attention in (A) was associated with the animated movie Castle in the Sky, whereas that in (B) was associated with a total lunar eclipse. The black bars in the figures indicate the time period when the posted tweets were analyzed to compute the term frequency. The red text in the tables indicates terms related to the target events and the text in parentheses shows the English translation.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems"], "article_id"=>694204, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g005", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_moderate_collective_attention_/694204", "title"=>"Examples of moderate collective attention.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:10:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048930"], "description"=>"<p>Tweet probability distribution, and the rankings of the popularity and the enhancement of key terms in Japanese tweets on (A) March 11, 2011 and (B) July 18, 2011. The collective attention in (A) was associated with the Tohoku-oki earthquake and tsunami, whereas that in (B) was associated with the FIFA Women's World Cup, 2011. The black bars in the figures indicate the time period when the posted tweets were analyzed to compute term frequency. The red text in the tables denotes terms related to the target events and the text in parentheses shows the English translation.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems"], "article_id"=>694201, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_strong_collective_attention_/694201", "title"=>"Examples of strong collective attention.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:10:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048928"], "description"=>"<p>Tweet probability distribution for March 11, 2011, the day of the Tohoku-oki earthquake, showing a burst-like increase and a sustained active state, whereas those for March 1 and 10 exhibit almost the same temporal profiles as those on normal days.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "tweet"], "article_id"=>694199, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Daily_tweet_activity_/694199", "title"=>"Daily tweet activity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:09:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048929"], "description"=>"<p>Intensity of collective attention (i.e., the value) in (A) 2010 and (B) 2011. (C) Distribution of values on weekdays, weekends, and national holidays. Only values are shown.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "js"], "article_id"=>694200, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distributions_of_the_JS_values_related_to_collective_attention_/694200", "title"=>"Distributions of the JS values related to collective attention.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:10:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048938"], "description"=>"<p>(A) Degree distribution of the RT networks related to the Tohoku-oki earthquake, FIFA Women's World Cup, 2011, Castle in the Sky, and total lunar eclipse. (B) Boxplots of the connected component sizes in the RT networks.</p>", "links"=>[], "tags"=>["Electrical engineering", "communications", "signal processing", "data mining", "anthropology", "Social anthropology", "Sociology", "Computational sociology", "social networks", "Social research", "Social systems", "rt"], "article_id"=>694209, "categories"=>["Information And Computing Sciences", "Engineering", "Sociology"], "users"=>["Kazutoshi Sasahara", "Yoshito Hirata", "Masashi Toyoda", "Masaru Kitsuregawa", "Kazuyuki Aihara"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0061823.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structural_features_of_RT_networks_/694209", "title"=>"Structural features of RT networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-30 01:10:09"}

PMC Usage Stats | Further Information

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Relative Metric

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