Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control
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{"title"=>"Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control", "type"=>"journal", "authors"=>[{"first_name"=>"Marcel", "last_name"=>"Salathé", "scopus_author_id"=>"12780442200"}, {"first_name"=>"Shashank", "last_name"=>"Khandelwal", "scopus_author_id"=>"35303036800"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "arxiv"=>"1105.4502", "scopus"=>"2-s2.0-80055065085", "pui"=>"362834457", "doi"=>"10.1371/journal.pcbi.1002199", "isbn"=>"1553734X", "sgr"=>"80055065085", "pmid"=>"22022249"}, "id"=>"a83ee20c-8c21-3ee6-bf98-0936e2e82864", "abstract"=>"There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC- estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks are greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.", "link"=>"http://www.mendeley.com/research/assessing-vaccination-sentiments-online-social-media-implications-infectious-disease-dynamics-contro-23", "reader_count"=>110, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>14, "Librarian"=>2, "Student > Doctoral Student"=>4, "Researcher"=>14, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>7, "Student > Master"=>18, "Other"=>4, "Student > Bachelor"=>10, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>14, "Librarian"=>2, "Student > Doctoral Student"=>4, "Researcher"=>14, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>7, "Student > Master"=>18, "Other"=>4, "Student > Bachelor"=>10, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>14, "Arts and Humanities"=>4, "Business, Management and Accounting"=>4, "Computer Science"=>28, "Earth and Planetary Sciences"=>1, "Energy"=>1, "Engineering"=>2, "Environmental Science"=>3, "Nursing and Health Professions"=>3, "Mathematics"=>4, "Medicine and Dentistry"=>19, "Sports and Recreations"=>1, "Physics and Astronomy"=>1, "Psychology"=>4, "Social Sciences"=>15, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Social Sciences"=>{"Social Sciences"=>15}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>4}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>4}, "Engineering"=>{"Engineering"=>2}, "Energy"=>{"Energy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>28}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>4}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>6, "United Kingdom"=>1, "France"=>1, "Spain"=>1}, "group_count"=>14}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/724074"], "description"=>"<p>The proportions of negative and positive sentiments are significantly different from the overall proportions in the entire opinionated network (with the exception of community E). (<b>B</b>) Effect of positive assortativity index (<i>r</i>) on relative risk increase (compared to risk at <i>r</i>∼0) of disease outbreaks that infect at least 3% of the population. Blue line shows best fit of linear regression (confidence interval based on standard error). (<b>C</b>) Relative risk increase (compared to risk at <i>r</i>∼0) of disease outbreaks of a given fraction of the population (on horizontal axis) for two values of assortativity index (<i>r</i>), 0.075 (red) and 0.145 (green). Note that the latter corresponds to <i>r</i> found in the opinionated network (see main text).</p>", "links"=>[], "tags"=>["sentiments", "dashed", "shows", "opinionated"], "article_id"=>394427, "categories"=>["Biological Sciences"], "users"=>["Marcel Salathé", "Shashank Khandelwal"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002199.g002", "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Proportion_of_negative_sentiments_p_in_the_network_communities_Dashed_line_shows_overall_proportion_in_the_opinionated_network_/394427", "title"=>"(A) Proportion of negative sentiments <i>p(-)</i> in the network communities. Dashed line shows overall proportion in the opinionated network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 13:45:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/723989"], "description"=>"<p>(<b>B</b>) Daily (gray) and 14 day moving average (blue) sentiment score during the same time. (<b>C</b>) Correlation between estimated vaccination rates for individuals older than 6 months, and sentiment score per HHS region (black dots) and states (gray dots). Numbers represent the ten regions as defined by the US Department of Human Health & Services. Lines shows best fit of linear regression (blue for regions, red for states).</p>", "links"=>[], "tags"=>["tweets", "relating", "influenza", "vaccination", "2009"], "article_id"=>394340, "categories"=>["Biological Sciences"], "users"=>["Marcel Salathé", "Shashank Khandelwal"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002199.g001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Total_number_of_negative_red_positive_green_and_neutral_blue_tweets_relating_to_influenza_A_H1N1_vaccination_during_the_Fall_wave_of_the_2009_pandemic_/394340", "title"=>"(A) Total number of negative (red), positive (green), and neutral (blue) tweets relating to influenza A(H1N1) vaccination during the Fall wave of the 2009 pandemic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 13:45:08"}

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  • {"unique-ip"=>"22", "full-text"=>"28", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"26", "full-text"=>"27", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"12", "full-text"=>"165", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"21", "full-text"=>"145", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}

Relative Metric

{"start_date"=>"2011-01-01T00:00:00Z", "end_date"=>"2011-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[304, 568, 702, 818, 927, 1027, 1118, 1206, 1285, 1357, 1427, 1500, 1564, 1636, 1705, 1773, 1840, 1909, 1974, 2039, 2106, 2170, 2234, 2296, 2358, 2423, 2484, 2546, 2606, 2673, 2734, 2795, 2857, 2921, 2984, 3046, 3100]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[355, 609, 735, 840, 944, 1029, 1109, 1193, 1255, 1320, 1391, 1469, 1540, 1608, 1683, 1753, 1824, 1905, 1967, 2042, 2117, 2189, 2240, 2300, 2392, 2452, 2509, 2569, 2635, 2710, 2772, 2846, 2882, 2956, 3014, 3086, 3139]}, {"subject_area"=>"/Medicine and health sciences/Infectious diseases", "average_usage"=>[335, 648, 777, 906, 1018, 1131, 1224, 1307, 1396, 1462, 1541, 1619, 1695, 1764, 1821, 1892, 1959, 2029, 2095, 2155, 2218, 2270, 2329, 2395, 2457, 2528, 2590, 2638, 2708, 2759, 2823, 2882, 2930, 2990, 3041, 3099, 3150]}, {"subject_area"=>"/Social sciences", "average_usage"=>[340, 584, 708, 809, 915, 1005, 1089, 1174, 1254, 1334, 1408, 1478, 1563, 1648, 1716, 1791, 1847, 1915, 1979, 2044, 2105, 2163, 2218, 2297, 2382, 2436, 2493, 2553, 2618, 2682, 2741, 2786, 2847, 2937, 2989, 3039, 3102]}, {"subject_area"=>"/Social sciences/Sociology", "average_usage"=>[426, 807, 983, 1078, 1202, 1297, 1394, 1505, 1611, 1713, 1819, 1918, 2007, 2092, 2190, 2261, 2341, 2445, 2522, 2612, 2692, 2754, 2830, 2908, 2987, 3076, 3157, 3246, 3319, 3431, 3527, 3602, 3670, 3725, 3772, 3821, 3879]}]}
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