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What We Read, What We Search: Media Attention and Public Attention Among 193 Countries

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We investigate the alignment of international attention of news media organizations within 193 countries with the expressed international interests of the public within those same countries from March 7, 2016 to April 14, 2017. We collect fourteen months of longitudinal data of online news from Unfiltered News and web search volume data from Google Trends and build a multiplex network of media attention and public attention in order to study its structural and dynamic properties. Structurally, the media attention and the public attention are both similar and different depending on the resolution of the analysis. For example, we find that 63.2% of the country-specific media and the public pay attention to different countries, but local attention flow patterns, which are measured by network motifs, are very similar. We also show that there are strong regional similarities with both media and public attention that is only disrupted by significantly major worldwide incidents (e.g., Brexit). Using Granger causality, we show that there are a substantial number of countries where media attention and public attention are dissimilar by topical interest. Our findings show that the media and public attention toward specific countries are often at odds, indicating that the public within these countries may be ignoring their country-specific news outlets and seeking other online sources to address their media needs and desires.

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What We Read, What We Search: Media Attention and Public Attention Among 193 Countries

  1. 1. What We Read, What We Search: Media Attention and Public Attention Among 193 Countries Haewoon Kwak Jisun An Joni Salminen Soon-Gyo Jung Bernard J. Jansen Qatar Computing Research Institute, Hamad Bin Khalifa University
  2. 2. What We Read, What We Search: Media Attention and Public Attention Among 193 Countries 2
  3. 3. What We Read, What We Search: Structures and Dynamics of the
 Multiplex Network of Media Attention and Public Attention Among 193 Countries Original title was… 3
  4. 4. What We Read, What We Search: Structures and Dynamics of the
 Multiplex Network of Media Attention and Public Attention Among 193 Countries Original title was… 4
  5. 5. What We Read, What We Search: Media Attention and Public Attention Among 193 Countries 5
  6. 6. What We Read, What We Search: Media Attention and Public Attention Among 193 Countries 1 Media attention? 2 Public attention? 6
  7. 7. Media attention • What news media pay attention to (report) The NYTimes pays attention to North Korea NYTimes North Korea 1 US North Korea US news media pay attention to North Korea !7
  8. 8. Public attention • What public pays attention to • There are several ways to measure public attention • Survey/Poll • Social media • (Google) search logs 2 !8
  9. 9. https://www.theguardian.com/technology/2017/jul/09/everybody-lies-how-google-reveals-darkest-secrets-seth-stephens-davidowitz !9
  10. 10. Why is this important? !10
  11. 11. Critical role of news media • Even in the era of social media, our understanding of the world is still dominantly shaped by news media. http://www.journalism.org/2016/07/07/pathways-to-news/ !11
  12. 12. Bias in news reporting • Many kinds of bias in news reporting have been studied. • Gatekeeping bias (also called selection bias): • What we see, read, and hear about incidents is a result of the gatekeeping by the journalists and news media. • Foreign news coverage reflects the social, economic, and political relationships among countries. !12
  13. 13. https://www.technologyreview.com/s/532036/data-mining-reveals-how-news-coverage-varies-around-the-world/ !13
  14. 14. Media outlets are not isolated but interact with the audience • These days journalists are trying to find a balance between what people ought to know and what people want to know • News industry becomes more and more competitive • People can easily access alternative online sources • User engagement is visible ✓However, the spatiotemporal alignment of the news coverage and public interests has been relatively unexplored !14
  15. 15. Our goal • We compare and contrast the media attention and public attention among countries using longitudinal datasets. • RQ1: How are media attention and public attention structurally aligned? • RQ2: What is the causal relationship between media attention and public attention? • RQ3: Are there topical aspects that affect the interaction between media attention and public attention? !15
  16. 16. Data collection - media attention • Unfiltered News run by Jigsaw http://unfiltered.news/ !16
  17. 17. Why Unfiltered.news? • Top 100 topics per country retrieve from Google News • Covers 100+ countries • Translate contents in major languages !17
  18. 18. Data collection - media attention • Top 100 popular topics for each country for every day from March 7, 2016 to April 14, 2017 • Co-mentions for each topic (context) • 1,322,730 records of media attention among countries • 152,557 unique co-mentions !18
  19. 19. Data collection - public attention • Google Trends Search Term Origin Location Time !19
  20. 20. Data collection - public attention • For each country, we collect search volumes about other countries from March 7, 2016 to April 14, 2017 e.g. Time-series of search volume about US and KR in Korea !20
  21. 21. Build the multiplex network NM: Media attention network (from Unfiltered News) NP: Public attention network (from Google Trends) + !21
  22. 22. Building daily NM and NP on t Media attention Public attention 193 countries Node 193 countries News media in ci cover cj on t Link from ci to cj Public in ci search cj on t !22
  23. 23. Build the weighted network NM: Superimposing daily NM over all t NP: Superimposing daily NP over all t !23
  24. 24. Country A Country B Topic 1 Topic 2 Topic 3 Country B Topic 5 … Topic 100 7 March 2016 Country B Topic 2 Topic 3 Topic 4 Topic 5 … Topic 100 10 March 2016 Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 … Country B 12 April 2016 weight=3 !24
  25. 25. Backbone extraction • Applying disparity filter proposed by [41] and extracting significant links (called a “backbone”) !25
  26. 26. Basic topological characteristics of media/public attention networks Backbone B( · ) !26
  27. 27. RQ1: How are media attention and public attention structurally aligned? !27
  28. 28. Afghanistan Iran Pakistan United States of America Albania Republic of Macedonia Algeria Egypt France Iraq Israel Libya Morocco Russia Saudi Arabia SyriaTunisia Turkey Yemen Angola Brazil China Mozambique Nigeria Portugal Anguilla The Bahamas Antigua and Barbuda Barbados Guyana Jamaica North Korea Trinidad and Tobago United Kingdom Argentina Bolivia Chile Colombia Cuba Ecuador Mexico Peru Spain Uruguay Venezuela Armenia Azerbaijan Georgia Germany Australia Bosnia and Herzegovina Fiji New Zealand Austria Greece Italy Switzerland Kazakhstan Turkmenistan Ukraine Bahrain Jordan Kuwait Oman Qatar United Arab Emirates Bangladesh Burma India Haiti Saint Kitts and Nevis Saint Lucia Belarus Belgium Cameroon Netherlands Belize Guatemala Taiwan Benin Gabon Gambia Niger Panama Bermuda Canada Bhutan Japan Croatia Montenegro Serbia Botswana Namibia South Africa Zimbabwe British Virgin Islands Brunei Philippines South Korea Bulgaria Romania Burkina Faso Mali Thailand Cambodia Indonesia Malaysia Vietnam Central African Republic Chad Senegal Cayman Islands Togo Hong Kong Costa Rica Nicaragua Slovenia Cyprus Czech Republic Slovakia Dominica Dominican Republic Puerto Rico Sudan El Salvador Honduras Eritrea Ethiopia Estonia Latvia Kenya Somalia Papua New Guinea Samoa Tonga Finland Norway Sweden French Guiana Martinique French Polynesia Sierra Leone Ghana Gibraltar Greenland Iceland Guadeloupe Guam Guinea Suriname Macau Hungary Sri Lanka Singapore Ireland Isle of Man Kyrgyzstan Tajikistan Uzbekistan Rwanda Tanzania Uganda Kiribati Lithuania Lebanon Liberia Liechtenstein Poland Luxembourg Madagascar Mauritius Réunion Malawi Zambia Maldives Malta Mauritania Moldova Monaco Montserrat Nepal New Caledonia Paraguay Burundi Mayotte San Marino Seychelles Solomon Islands Swaziland United States Virgin Islands B(NM): Media attention Afghanistan India Iran Pakistan Turkey United States of America Albania France Germany Greece Italy United KingdomAlgeria Egypt Japan Morocco Saudi Arabia Spain Syria Tunisia Angola Brazil Portugal South Africa AnguillaBarbados British Virgin Islands Canada Dominica Dominican Republic Guyana Jamaica Puerto Rico Saint Kitts and Nevis Saint Lucia Trinidad and Tobago Antigua and Barbuda Argentina Chile Colombia Mexico Paraguay Uruguay Armenia Azerbaijan Georgia Russia Ukraine Australia New Zealand Austria Croatia Switzerland China Bahrain Bangladesh Kuwait Philippines Qatar United Arab Emirates Belarus Lithuania Poland Belgium Netherlands Belize Guatemala Honduras Benin Ghana Nigeria Togo Bermuda Bhutan Nepal Thailand Bolivia Peru Bosnia and Herzegovina Montenegro Serbia Slovenia Botswana Zambia Zimbabwe Guadeloupe Hong Kong Singapore United States Virgin Islands Brunei Indonesia Malaysia Bulgaria Burkina Faso Gabon Mali Burma Burundi Kenya Rwanda Tanzania Uganda Cambodia Vietnam Cameroon Cayman Islands Cuba Central African Republic Senegal Chad Libya Sudan Macau Venezuela Costa Rica Nicaragua Panama Ecuador Cyprus Czech Republic Slovakia Haiti Israel Lebanon El Salvador Eritrea Ethiopia Estonia Finland Latvia Norway Sweden Somalia Fiji French Guiana Martinique Suriname French Polynesia New Caledonia Gambia Guinea Gibraltar Greenland Iceland Grenada Guam South Korea Hungary Iraq Ireland Isle of Man Jordan Oman Kazakhstan Uzbekistan Kiribati Papua New Guinea Samoa Taiwan Kyrgyzstan Liberia Liechtenstein Luxembourg Madagascar Malawi Maldives Sri Lanka Malta Mauritania Mauritius Mayotte Moldova Romania Monaco Mongolia Montserrat Mozambique Namibia Niger Republic of Macedonia Réunion San Marino Yemen Seychelles Sierra Leone Solomon Islands Swaziland Tajikistan The Bahamas Tonga Turkmenistan Turks and Caicos Islands B(NP): Public attention !28
  29. 29. Node Dyadic relationships Triad Community !29
  30. 30. If one country gets high media attention from other countries, does it also get high public attention? !30
  31. 31. Centrality of a country in B(NM) and B(NP) positively correlate One country gets high media (public) attention from other countries, it also gets high public (media) attention. !31
  32. 32. Do media and public in one country pay the most attention to the same country? !32
  33. 33. Top@k neighbors: Top k countries who get the attention from one country Country i Country j Country k Country l !33
  34. 34. Top@k neighbors: Top k countries who get the attention from one country Country i Country j Country k Country l Top@1 neighbor !34
  35. 35. Top@k neighbors: Top k countries who get the attention from one country Country i Country j Country k Country l Top@2 neighbors !35
  36. 36. Overlap of top@k neighbors of each country between NM and NP !36
  37. 37. Overlap of top@k neighbors of each country between NM and NP In 63.2% of the countries, the media and the public pay the most attention (k=1) to different countries. Media’s heavy attention to the United States is noticeable. Temporally stable; but worldwide convergence can happen. !37
  38. 38. The media and the public pay attention to other countries in different ways by focusing on different countries with different strengths This experiment is omitted in the presentation. Please check our paper or slide 67-69
  39. 39. Attention flow among three countries: Network motifs • (Usually 3- or 4-) sized subgraphs that repeat in a given network !39
  40. 40. Proportions of each motif (compared to a null model) are different according to the types of networks Superfamilies of Evolved and Designed Networks Ron Milo, Shalev Itzkovitz, Nadav Kashtan, Reuven Levitt, Shai Shen-Orr, Inbal Ayzenshtat,Michal Sheffer, Uri Alon Science, 303(5663), 2004 !40
  41. 41. Similar motif profiles in B(NM) and B(NP) • FFL !41
  42. 42. Similar motif profiles in B(NM) and B(NP) • FFL FFL / Double feedback loop Transitive hierarchy exists in media and public attention e.g. [Algeria → France, Algeria → the U.S., France → the U.S.] !42
  43. 43. Similar motif profiles in B(NM) and B(NP) • FFL Fully connected triad Geographically closely located countries pay attention to each other e.g. [China, Hong Kong, Macau] !43
  44. 44. Attention flow among regions • Recent studies on media attention report: • Strong regionalism [21] • ‘Global village’ trend [22] • International news agencies play an essential role !44
  45. 45. Community structures in B(NM) and B(NP) by InfoMap [39] • ‘Global village’ trend in media attention: 80 countries in one community Clearer geographical splits !45
  46. 46. Community structures by InfoMap [39] The core of civilizations proposed by Huntington [15] is still observed. !46 The trend of the global village and other variations require new models and explanations.
  47. 47. RQ2: What is the causal relationship between media attention and public attention? !47
  48. 48. Granger causality • One time series X Granger-causes the other time series Y when past values of X can improve the explanation of the current value of Y compared to when past values of Y are used alone. https://en.wikipedia.org/wiki/Granger_causality !48
  49. 49. Granger-causal relationship among regions The media attention or the public attention within the same region is likely to Granger-cause to its counterpart. !49
  50. 50. What influences the Granger- causal relationships? • Distance between two countries • Out-degree in B(NM) • GDP of the source country • Internet penetration in the source country • GDP of the destination country !50
  51. 51. What influences the Granger- causal relationships? • Distance between two countries • Out-degree in B(NM) • GDP of the source country • Internet penetration in the source country • GDP of the destination country Consistent with previous work on newsworthiness of events (news values) !51
  52. 52. RQ3: Are there topical aspects that affect the interaction between media attention and public attention? !52
  53. 53. Why one country gets media attention !53
  54. 54. Why one country gets media attention 5 in G8 countries are covered with politics, but France, Italy, and Japan are covered with travel. !54
  55. 55. Why one country gets media attention Negative stereotyping of the third world by media attention in the early 2000s is disappearing? !55
  56. 56. How one country is covered by countries in a specific region • The United States, the United Kingdom, and Russia are always covered with ‘politics’ by any region • Countries get covered with ‘travel’ more by the same region than other regions • Several consistent connections between a country and a topic across the regions, such as Brazil and sports, and most of the Middle Eastern countries and politics. !56
  57. 57. Top 2 topics that lead to the most Granger-causal relationships • Sports and travel • High engagement of the public in the entertainment- oriented news (often called soft news [37]) and supplies of such news by the media !57
  58. 58. Summary (1): Structural alignment of media attention and public attention • The importance of a country is positively correlated. • To whom one country pays significant attention is different. • The distribution of attention across neighbors is different; public attention being distributed more equally than the media attention. • The relationship among three countries is similar. • Media attention shows the trend of a global village, while public attention shows clearer geographical splits. !58
  59. 59. Summary (2): Interaction between media attention and public attention • Media attention and the public attention within the same region strongly associate with each other. • Along with distance, country properties influence the Granger causality. • There are some variations in the interplay between the media attention and the public attention according to the topics.
  60. 60. My Two Riyals … • Unfiltered News and Google Trends are good data sources to track media and public attention. • The media and public attention are often at odds • The media may be losing the power of agenda setting in the online era, and the public may be seeking other online sources to address their media needs. !60
  61. 61. @haewoon http://haewoon.io/ !61 https://dl.acm.org/citation.cfm?id=3186137
  62. 62. Backup slides !62
  63. 63. Multiplex network • A multilayer network is a network made up by multiple layers, each of which represents a given operation mode, social circle, or temporal instance. • In a multiplex network, each type of interaction between the nodes is described by a single layer network. Text from http://cosnet.bifi.es/network-theory/multiplex-networks/ Image from https://github.com/gajduk/social-networks-analysis-wan-bms 3 !63
  64. 64. Attention flow among regions Source Destination Source !64
  65. 65. Attention flow among regions Strong regionalism Source Destination Source !65
  66. 66. Attention flow among regions Emergence of Asia as a receiver of media attention Source Destination Source !66
  67. 67. Distribution of attention strengths Image from https://community.lithium.com/t5/Science-of-Social-Blog/The-Economics-of-90-9-1-The-Gini-Coefficient-with-Cross/ba-p/5466 Gini Index !67
  68. 68. Gini coeff. of the attention strengths for each node in NM and NP Uniform Distribution Skewed Distribution !68
  69. 69. Gini coeff. of the attention strengths for each node in NM and NP The public attention goes more equally to other countries than media attention does. Uniform Distribution Skewed Distribution Patterns of paying attention are quite diverse !69
  70. 70. Well-known names of each motif Feed forward loop (FFL)Fan-out Cascade Fan-in Fully connected triad Double feedback loop !70
  71. 71. Limitations • We model media attention and public attention from a single service each. • We set the scope of the analysis as the foreign news coverage and the corresponding web search. !71

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