Twitter-Based Analysis of the Dynamics of Collective Attention to Political Parties
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{"title"=>"Twitter-based analysis of the dynamics of collective attention to political parties", "type"=>"journal", "authors"=>[{"first_name"=>"Young Ho", "last_name"=>"Eom", "scopus_author_id"=>"8454032000"}, {"first_name"=>"Michelangelo", "last_name"=>"Puliga", "scopus_author_id"=>"15077156900"}, {"first_name"=>"Jasmina", "last_name"=>"Smailovic", "scopus_author_id"=>"55787266400"}, {"first_name"=>"Igor", "last_name"=>"Mozetic", "scopus_author_id"=>"6506490886"}, {"first_name"=>"Guido", "last_name"=>"Caldarelli", "scopus_author_id"=>"55139905100"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84940546981", "doi"=>"10.1371/journal.pone.0131184", "sgr"=>"84940546981", "arxiv"=>"1504.06861", "isbn"=>"19326203 (ISSN)", "pmid"=>"26161795", "issn"=>"19326203", "pui"=>"605775877"}, "id"=>"bf4f16b2-b0ba-3c73-a306-3f9d56999591", "abstract"=>"Large-scale data from social media have a significant potential to describe complex phenomena in real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the elections outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.", "link"=>"http://www.mendeley.com/research/twitterbased-analysis-dynamics-collective-attention-political-parties", "reader_count"=>57, "reader_count_by_academic_status"=>{"Researcher"=>15, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>10, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>4, "Professor"=>5}, "reader_count_by_user_role"=>{"Researcher"=>15, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>10, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>4, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>1, "Business, Management and Accounting"=>4, "Physics and Astronomy"=>5, "Psychology"=>7, "Social Sciences"=>12, "Computer Science"=>16, "Economics, Econometrics and Finance"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>12}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>7}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>16}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>4}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"United States"=>1, "Italy"=>1, "United Kingdom"=>1, "Malaysia"=>1, "Slovenia"=>1, "Switzerland"=>2, "Germany"=>1, "Spain"=>1}, "group_count"=>16}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2170457"], "description"=>"<p>Cumulative distribution functions of the log ratio for each party are represented in (A) <i>Euro14</i>. (C)<i>Italy13</i>. (E) <i>Bulgaria13</i>. The Q-Q plots of the log ratio <i>r</i>(<i>t</i>) for each party are also represented in (B)<i>Euro14</i>. (D) <i>Italy13</i>. (F) <i>Bulgaria13</i>. The theoretical quantile is based on normal distribution. In the Q-Q plot, if the points lie on <i>y</i> = <i>x</i>, it means the log ratio follow a normal distribution.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479200, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Normality_of_the_logarithmic_ratio_r_p_t_log_V_p_t_1_V_p_t_of_two_consecutive_tweet_volumes_of_party_p_/1479200", "title"=>"Normality of the logarithmic ratio <i>r</i><sub><i>p</i></sub>(<i>t</i>) = <i>log</i>(<i>V</i><sub><i>p</i></sub>(<i>t</i> + 1)/<i>V</i><sub><i>p</i></sub>(<i>t</i>)) of two consecutive tweet volumes of party <i>p</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170455"], "description"=>"<p>Autocorrelation coefficient <i>R</i><sub><i>p</i></sub>(<i>τ</i>) is given by </p><p></p><p></p><p></p><p><mi>R</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mi>τ</mi><mo stretchy=\"false\">)</mo><mo>=</mo><p><mn>1</mn></p><p></p><p><mi>t</mi><mi>e</mi></p><mo>−</mo><mi>τ</mi><p></p><p></p><p></p><p><mo>∑</mo></p><p><mi>t</mi><mo>=</mo><mn>0</mn></p><p></p><p><mi>t</mi><mi>e</mi></p><mo>−</mo><mn>1</mn><mo>−</mo><mi>τ</mi><p></p><p></p><p></p><p></p><p><mo stretchy=\"false\">(</mo></p><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>−</mo><p><mo>〈</mo><mi>V</mi><mo>〉</mo></p><mo stretchy=\"false\">)</mo><mo stretchy=\"false\">(</mo><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo>+</mo><mi>τ</mi><mo stretchy=\"false\">)</mo><mo>−</mo><p><mo>〈</mo></p><p><mi>V</mi><mo>′</mo></p><mo>〉</mo><p></p><mo stretchy=\"false\">)</mo><p></p><p></p><p><mi>σ</mi><mi>p</mi></p><p><mi>σ</mi><mi>p</mi><mo>′</mo></p><p></p><p></p><p></p><p></p><p></p><p></p><p></p>. Here ⟨<i>V</i>⟩ (⟨<i>V</i>′⟩) is the average daily tweet volume for party <i>p</i> from day <i>t</i> = 0 (<i>t</i> = <i>τ</i>) to day <i>t</i> = <i>t</i><sub><i>e</i></sub>−1−<i>τ</i> (<i>t</i> = <i>t</i><sub><i>e</i></sub>−<i>τ</i>), <i>σ</i><sub><i>p</i></sub> (<p></p><p></p><p></p><p><mi>σ</mi><mi>p</mi><mo>′</mo></p><p></p><p></p><p></p>) is the standard deviation, and <i>t</i><sub><i>e</i></sub> is the election day. (A)<i>Euro14</i>. (B) <i>Italy13</i>. (C) <i>Bulgaria13</i>.<p></p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479198, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Autocorrelation_of_daily_tweet_volume_for_each_political_party_/1479198", "title"=>"Autocorrelation of daily tweet volume for each political party.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170452"], "description"=>"<p>The ordering of parties (i.e., the numbers in parentheses) is based on actual ranking in the election. (A) <i>Euro14</i>. 1st: PD. 2nd: M5S. 3rd: FI. 4th: LN. 5th: NCD-UdC. 6th: AET. 7th: FdI-AN. (B) <i>Italy13</i>. 1st: M5S. 2nd: PD. 3rd: PdL. 4th: SC. 5th: LN. 6th: SEL. (C) <i>Bulgaria13</i>. 1st: GERB. 2nd: BSP. 3rd: DPS. 4th: ATAKA.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479195, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g001", "stats"=>{"downloads"=>0, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Daily_tweet_volume_for_each_party_around_elections_/1479195", "title"=>"Daily tweet volume for each party around elections.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170466"], "description"=>"<p>Time stamps in <i>Euro14</i> and <i>Italy</i> are in local time while time stamps in <i>Bulgaria13</i> are in Greenwich Mean Time (GMT). There is a three-hours difference between GMT and Bulgarian time. <i>T</i><sub><i>i</i></sub> represents the initial day of considered data. <i>T</i><sub><i>e</i></sub> is the election day. <i>T</i><sub><i>f</i></sub> represents the final day of considered data. One-day is defined a time interval from 00:00:00 to 23:59:59 in considered time. <i>N</i><sub><i>T</i></sub> represents the total number of considered tweets for given time interval from <i>T</i><sub><i>i</i></sub> to <i>T</i><sub><i>e</i></sub>-1 posted in local language. <i>N</i><sub><i>P</i></sub> represents the number of considered political parties.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479209, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.t001", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Description_of_Twitter_data_set_/1479209", "title"=>"Description of Twitter data set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170465"], "description"=>"<p></p><p></p><p></p><p></p><p></p><p><mi>V</mi><mo>‾</mo></p><mi>p</mi><p></p><mo stretchy=\"false\">(</mo><mi>λ</mi><mo stretchy=\"false\">)</mo><p></p><p></p><p></p> is given by <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131184#pone.0131184.e017\" target=\"_blank\">Eq 6</a>. The numbers in parentheses represent actual rankings of the parties in the election. (A)<i>Euro14</i>. (B) <i>Italy13</i>. (C) <i>Bulgaria13</i>.<p></p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479208, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g007", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_ranking_determined_by_tweet_volume_V_8254_p_955__averaged_from_the_day_before_the_election_to_the_964_days_before_the_election_/1479208", "title"=>"Predicted ranking determined by tweet volume V‾p(λ) averaged from the day before the election to the <i>τ</i> days before the election.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170464"], "description"=>"<p>In the GBM model, the expected volume <i>V</i>(<i>t</i>) at time <i>t</i> is given by </p><p></p><p></p><p></p><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>=</mo><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mn>0</mn><mo stretchy=\"false\">)</mo><mi>e</mi><mi>x</mi><mi>p</mi><mo stretchy=\"false\">(</mo><mo stretchy=\"false\">(</mo><mi>μ</mi><mo>−</mo><p></p><p><mi>σ</mi><mn>2</mn></p><mn>2</mn><p></p><mo stretchy=\"false\">)</mo><mi>t</mi><mo stretchy=\"false\">)</mo><p></p><p></p><p></p>. In the GBM+<i>σ</i> model, <p></p><p></p><p></p><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>=</mo><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mn>0</mn><mo stretchy=\"false\">)</mo><mi>e</mi><mi>x</mi><mi>p</mi><mo stretchy=\"false\">(</mo><mo stretchy=\"false\">(</mo><mi>μ</mi><mo>−</mo><p></p><p><mi>σ</mi><mn>2</mn></p><mn>2</mn><p></p><mo stretchy=\"false\">)</mo><mi>t</mi><mo>+</mo><mi>σ</mi><mo stretchy=\"false\">)</mo><p></p><p></p><p></p> while <p></p><p></p><p></p><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>=</mo><p><mi>V</mi><mi>p</mi></p><mo stretchy=\"false\">(</mo><mn>0</mn><mo stretchy=\"false\">)</mo><mi>e</mi><mi>x</mi><mi>p</mi><mo stretchy=\"false\">(</mo><mo stretchy=\"false\">(</mo><mi>μ</mi><mo>−</mo><p></p><p><mi>σ</mi><mn>2</mn></p><mn>2</mn><p></p><mo stretchy=\"false\">)</mo><mi>t</mi><mo>−</mo><mi>σ</mi><mo stretchy=\"false\">)</mo><p></p><p></p><p></p> in the GBM−<i>σ</i> model. The values of parameters <i>μ</i>, <i>σ</i>, and <i>V</i>(0) are given in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131184#pone.0131184.t003\" target=\"_blank\">Table 3</a>.<p></p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479206, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g006", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dynamics_of_daily_tweet_volume_for_each_party_represented_by_data_and_by_the_GBM_model_/1479206", "title"=>"Dynamics of daily tweet volume for each party represented by data and by the GBM model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170460"], "description"=>"<p>Here <i>V</i><sub><i>p</i></sub>(<i>t</i>) is the tweet volume of the party <i>p</i> at time <i>t</i>.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479203, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g005", "stats"=>{"downloads"=>2, "page_views"=>30, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_time_t_and_log_ratio_r_p_t_log_V_p_t_1_V_p_t_for_each_party_p_/1479203", "title"=>"Scatter plot of time <i>t</i> and log ratio <i>r</i><sub><i>p</i></sub>(<i>t</i>) = <i>log</i>(<i>V</i><sub><i>p</i></sub>(<i>t</i> + 1)/<i>V</i><sub><i>p</i></sub>(<i>t</i>)) for each party <i>p</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170468"], "description"=>"<p>The expectation value <i>V</i><sub><i>p</i></sub>(<i>t</i>) of daily tweet volume of party <i>p</i> at time <i>t</i> given by a GBM is <i>V</i><sub><i>p</i></sub>(<i>t</i>) = <i>V</i><sub><i>p</i></sub>(0)<i>exp</i>((<i>μ</i>−<i>σ</i><sup>2</sup>/2)<i>t</i> + <i>σW</i>(<i>t</i>)) where <i>W</i>(<i>t</i>) is a Wiener process or a Brownian motion.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479211, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.t003", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_to_describe_the_dynamics_of_daily_tweet_volume_of_political_parties_as_a_geometric_Brownian_motion_GBM_/1479211", "title"=>"Parameters to describe the dynamics of daily tweet volume of political parties as a geometric Brownian motion (GBM).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170453"], "description"=>"<p>Each volume in CDF is normalized by the average ⟨<i>V</i>⟩. (A) CDF of daily tweet volume of <i>Euro14</i>. (B) Q-Q plot of <i>Euro14</i>. (C) CDF of daily tweet volume of <i>Italy13</i>. (D) Q-Q plot of <i>Italy13</i>. (E) CDF of daily tweet volume in <i>Bulgaria13</i>. (f) Q-Q plot in <i>Bulgaria13</i>. Note that Q-Q plot is for logarithm of daily tweet volume. Theoretical quantile in the Q-Q plot is based on normal distribution. Thus if the points in the Q-Q plot lie on <i>y</i> = <i>x</i> line, the daily tweet volume follows a log-normal distribution since the logarithm of the volume follow a normal distribution.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479196, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.g002", "stats"=>{"downloads"=>3, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cumulative_distribution_functions_CDF_of_daily_tweet_volumes_A_C_E_and_Q_Q_plots_of_logarithms_of_daily_tweet_volumes_for_each_political_party_B_D_F_/1479196", "title"=>"Cumulative distribution functions (CDF) of daily tweet volumes (A, C, E) and Q-Q plots of logarithms of daily tweet volumes for each political party (B, D, F).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-10 03:28:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/2170467"], "description"=>"<p>The official sources of election results are provided on [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131184#pone.0131184.ref035\" target=\"_blank\">35</a>](<i>Euro14</i>), [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131184#pone.0131184.ref036\" target=\"_blank\">36</a>](<i>Italy13</i>), and [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131184#pone.0131184.ref037\" target=\"_blank\">37</a>] (<i>Bulgaria13</i>) respectively.</p>", "links"=>[], "tags"=>["presence", "trend", "Parti", "phenomena", "i.e", "effort", "information", "indicator", "ratio", "tweet volume", "media", "model", "researcher", "Brownian Motion", "fluctuation", "issue", "tweet volumes", "logarithm", "stakeholder", "nature", "political", "election outcome", "term", "analysis", "autocorrelation", "proxy", "Collective Attention", "performance", "volume moves", "world", "time window", "data", "kind", "evolution", "dynamic", "dynamics", "quantity", "signal"], "article_id"=>1479210, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Young-Ho Eom", "Michelangelo Puliga", "Jasmina Smailović", "Igor Mozetič", "Guido Caldarelli"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0131184.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Description_of_considered_political_parties_for_each_election_/1479210", "title"=>"Description of considered political parties for each election.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-10 03:28:36"}

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  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"11", "full-text"=>"16", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"15", "full-text"=>"14", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"20", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"21", "full-text"=>"20", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

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