Performance of Social Network Sensors during Hurricane Sandy
Publication Date
February 18, 2015
Journal
PLOS ONE
Authors
Yury Kryvasheyeu, Haohui Chen, Esteban Moro, Pascal Van Hentenryck, et al
Volume
10
Issue
2
Pages
e0117288
DOI
https://dx.plos.org/10.1371/journal.pone.0117288
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0117288
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25692690
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333288
Europe PMC
http://europepmc.org/abstract/MED/25692690
Web of Science
000350061500047
Scopus
84923309286
Mendeley
http://www.mendeley.com/research/performance-social-network-sensors-during-hurricane-sandy-1
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Mendeley | Further Information

{"title"=>"Performance of social network sensors during Hurricane Sandy", "type"=>"journal", "authors"=>[{"first_name"=>"Yury", "last_name"=>"Kryvasheyeu", "scopus_author_id"=>"56525305900"}, {"first_name"=>"Haohui", "last_name"=>"Chen", "scopus_author_id"=>"56524793800"}, {"first_name"=>"Esteban", "last_name"=>"Moro", "scopus_author_id"=>"7005409234"}, {"first_name"=>"Pascal", "last_name"=>"Van Hentenryck", "scopus_author_id"=>"55667017700"}, {"first_name"=>"Manuel", "last_name"=>"Cebrian", "scopus_author_id"=>"8724466500"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "arxiv"=>"1402.2482", "scopus"=>"2-s2.0-84923309286", "sgr"=>"84923309286", "pui"=>"602339259", "isbn"=>"19326203", "pmid"=>"25692690", "doi"=>"10.1371/journal.pone.0117288"}, "id"=>"68d824f8-71f2-3be3-a44f-28eff013b53e", "abstract"=>"Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the \"friendship paradox\", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple \"sentiment sensing\" technique that can detect and locate disasters.", "link"=>"http://www.mendeley.com/research/performance-social-network-sensors-during-hurricane-sandy-1", "reader_count"=>40, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>3, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>3, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Veterinary Science and Veterinary Medicine"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>8, "Earth and Planetary Sciences"=>1, "Engineering"=>3, "Environmental Science"=>2, "Materials Science"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>1, "Psychology"=>2, "Social Sciences"=>13}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>13}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>2}, "Engineering"=>{"Engineering"=>3}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>1, "Brazil"=>1}, "group_count"=>5}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1910635"], "description"=>"<p>The path of the hurricane from the moment of its formation until dissipation is accompanied by the approximate extent of the hurricane force winds. Three threshold levels distinguished by shading correspond to the hurricane forces between Categories 1 and 3 (34, 50 and 64 knots respectively). An outer extent of the Category 1 winds is outlined in red and serves as a border of the area directly affected by the hurricane.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312149, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_track_and_approximate_extent_of_Hurricane_Sandy_combined_with_the_heatmap_of_geolocated_tweets_density_/1312149", "title"=>"The track and approximate extent of Hurricane Sandy, combined with the heatmap of geolocated tweets density.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910636"], "description"=>"<p>Histograms show the number of messages over time (horizontal axis represents an offset in hours with respect to the hurricane landing time at 00:00 UTC on October 30 2012) for three different levels of filtering. The filtering is implemented to discard messages that have hurricane-related keywords, but were generated prior to October 22 2012, when Hurricane Sandy was officially named (approximately at -200 hours on the time axis). Strong filtering avoids this early noise of irrelevant messages that may skew the estimate of an entry time. The most reliable form of such filtering is achieved by including only those messages that have “sandy” as part of a text or as a hashtag.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312150, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_different_levels_of_content_filtering_/1312150", "title"=>"Effect of different levels of content filtering.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910637"], "description"=>"<p>Analysis shows that users who appear in the dataset early demonstrate higher level of activity and are characterized by higher counts of friends and followers (occupy a more central network position). These features are especially pronounced in the pre-landing stage of the hurricane history (landing time at 00:00 UTC on October 30 2012 is taken as a reference zero point). The fact that both activity and centrality correlate with entry time (awareness) suggests that the “friendship paradox” holds and sensor groups have an advantage of awareness lead-time, the magnitude of which is to be established.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312151, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_activity_A_and_mean_number_of_friends_and_followers_B_for_users_as_a_function_of_their_entry_time_/1312151", "title"=>"Average activity (A) and mean number of friends and followers (B) for users as a function of their entry time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910643"], "description"=>"<p>Figure shows a distribution of messages over time (horizontal axis represents an offset in hours with respect to the hurricane landing time at 00:00 UTC on October 30 2012), discretized on a daily basis with diurnal oscillations (and discrete steps in cumulative representation) smoothed out by the Gaussian kernel density estimation with 8-hour bandwidth. The inset shows simple daily count histograms, which peak in the landing day, with the sensor group activity at significantly higher level. Left shift of the sensor group’s cumulative distribution confirms that it consistently leads in terms of the entry time (awareness).</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312153, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Typical_cumulative_distribution_functions_of_messages_posted_by_a_random_control_group_and_its_corresponding_sensor_group_/1312153", "title"=>"Typical cumulative distribution functions of messages posted by a random control group and its corresponding sensor group.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910644"], "description"=>"<p>The lead-time magnitude and its variance (A) both decrease with an increasing sample size. Geography plays an important role in determining the scale of the awareness advantage. Longest lead-time is achieved through the combination of network centrality and geographical relevance in the case of “control out—sensor in” combination. The geographical factor outweighs the network effect, as illustrated by the positive lead-times (or rather lag times in this case) for the “control in—sensor out” combination. Relative under-performance of the actual data against a randomized null model (B) may be caused by the exogenous, rather than endogenous, mode of information spread and the correlation between centrality and tweeting frequency.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312154, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Lead_time_magnitude_for_control_and_sensor_groups_of_various_combinations_of_geographical_origin_A_and_comparison_against_a_null_model_with_shuffled_timestamps_B_/1312154", "title"=>"Lead-time magnitude for control and sensor groups of various combinations of geographical origin (A) and comparison against a null model with shuffled timestamps (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910645"], "description"=>"<p>Comparison of the proprietary Topsy algorithm against freely available alternatives shows that sentiment trends over time (horizontal axis represents an offset in hours with respect to the hurricane landing time at 00:00 UTC on October 30 2012) are consistent between different tools. Positive (<i>posemo)</i> and negative (<i>negemo</i>)emotion scores provided by Linguistic Inquiry and Word Count library were combined into a single polarity measure as <i>score</i> = <i>posemo</i> -1.5·<i>negemo</i>, and the result of SentiStrength was shifted upwards by approximately 10% of its peak value.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312155, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temporal_evolution_of_sentiment_measured_by_three_different_techniques_Topsy_LIWC_and_SentiStrength_/1312155", "title"=>"Temporal evolution of sentiment measured by three different techniques (Topsy, LIWC and SentiStrength).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910647"], "description"=>"<p>Horizontal axis represents an offset in hours with respect to the hurricane landing time at 00:00 UTC on October 30 2012. Sentiment trends exhibit high level of random noise when aggregated on the basis of short time steps (A-D). If averaged over a longer period (E-H), an overall positive baseline level appears, which temporarily drops into negativity in the lead up to and the aftermath of the Hurricane Sandy landing (approximately from -100 to +100 hours). There is no discernible difference, i.e. detectable horizontal shift, in the temporal evolution of sentiment between control and sensor groups, suggesting that the emotional response is situational and universal. Geographical relevance causes the magnitude shift, with the groups affected by the hurricane demonstrating more negative average sentiment (panels B and C, or F and G).</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312157, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hourly_A_D_and_daily_E_H_sentiment_trends_for_control_and_sensor_groups_of_different_geography_as_identified_in_the_panel_titles_/1312157", "title"=>"Hourly (A-D) and daily (E-H) sentiment trends for control and sensor groups of different geography, as identified in the panel titles.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910648"], "description"=>"<p>We monitor the fraction of positive (solid green line for control and dashed green for sensor groups) and negative messages (solid red for control and dashed red for sensor groups) in the total volume of all tweets. Time is taken as an offset in hours with respect to the hurricane landing time at 00:00 UTC on October 30 2012. During the most severe stage of the hurricane, in the anticipation of and after its landing, the composition undergoes transition from predominantly positive to predominantly negative.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312158, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Daily_trends_in_the_composition_of_the_message_stream_/1312158", "title"=>"Daily trends in the composition of the message stream.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910649"], "description"=>"<p>The polarity of sentiment is highlighted by green (positive average sentiment) or red (negative average sentiment). The density of messages is represented by the transparency of the color, where more solid shading indicates higher activity. At the early stage (top panel), prevalent sentiment is either neutral or positive, and the interest in the hurricane is comparatively low, except for the Miami area. Close to the landing time (bottom) the activity increases and the sentiment in the area affected by the hurricane is overwhelmingly negative. Unaffected regions remain neutral or positive.</p>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312159, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_density_and_sentiment_for_messages_posted_during_an_hour_long_period_at_17_00_EDT_October_25_top_and_20_00_EDT_October_29_bottom_/1312159", "title"=>"Comparison of density and sentiment for messages posted during an hour-long period at 17:00 EDT October 25 (top) and 20:00 EDT October 29 (bottom).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-18 03:26:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/1910666", "https://ndownloader.figshare.com/files/1910667", "https://ndownloader.figshare.com/files/1910670", "https://ndownloader.figshare.com/files/1910672", "https://ndownloader.figshare.com/files/1910675", "https://ndownloader.figshare.com/files/1910677", "https://ndownloader.figshare.com/files/1910678", "https://ndownloader.figshare.com/files/1910679", "https://ndownloader.figshare.com/files/1910680"], "description"=>"<div><p>Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters.</p></div>", "links"=>[], "tags"=>["network topology", "performance", "awareness advantage", "Hurricane Sandy", "Social Network Sensors", "Modern communication platforms", "emergency preparedness", "Emotional response", "networks sensor method", "Twitter messages", "disaster management", "user", "Hurricane Sandy Information flow"], "article_id"=>1312176, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Yury Kryvasheyeu", "Haohui Chen", "Esteban Moro", "Pascal Van Hentenryck", "Manuel Cebrian"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0117288.s001", "https://dx.doi.org/10.1371/journal.pone.0117288.s002", "https://dx.doi.org/10.1371/journal.pone.0117288.s003", "https://dx.doi.org/10.1371/journal.pone.0117288.s004", "https://dx.doi.org/10.1371/journal.pone.0117288.s005", "https://dx.doi.org/10.1371/journal.pone.0117288.s006", "https://dx.doi.org/10.1371/journal.pone.0117288.s007", "https://dx.doi.org/10.1371/journal.pone.0117288.s008", "https://dx.doi.org/10.1371/journal.pone.0117288.s009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_Social_Network_Sensors_during_Hurricane_Sandy_/1312176", "title"=>"Performance of Social Network Sensors during Hurricane Sandy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-02-18 03:26:23"}

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  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"27", "full-text"=>"34", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"16", "full-text"=>"13", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"16", "full-text"=>"34", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"20", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"16", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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