Polarization Partisanship And Junk News V22
Polarization, Partisanship and Junk News Consumption over Social Media in the USCOMPROP DATA MEMO 2018.1 / FEBRUARY 6, 2018Vidya NarayananOxford anVlad hn Bence KollanyiOxford iLisa-Maria NeudertOxford tPhilip N. HowardOxford ACTWhat kinds of social media users read junk news? We examine the distribution of the most significant sources ofjunk news in the three months before President Donald Trump’s first State of the Union Address. Drawing on alist of sources that consistently publish political news and information that is extremist, sensationalist,conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of suchcontent is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network ofTrump supporters shares the widest range of known junk news sources and circulates more junk news than all theother groups put together; (2) on Facebook, extreme hard right pages—distinct from Republican pages—sharethe widest range of known junk news sources and circulate more junk news than all the other audiences puttogether; (3) on average, the audiences for junk news on Twitter share a wider range of known junk news sourcesthan audiences on Facebook’s public pages.POLARIZATION ON SOCIAL MEDIASocial media has become an important source of newsand information in the United States. An increasingnumber of users consider platforms such as Twitterand Facebook a source of news. At importantmoments of political and military crises, social mediausers not only share substantial amounts ofprofessional news, but also share extremist,sensationalist, conspiratorial, masked commentary,fake news and other forms of junk news.1,2News on social media also reaches usersindirectly, when they browse social media for otherpurposes. With more than 2 billion monthly activeusers, Facebook is the most popular social medianetwork. The Reuters Digital News Report 2017 findsthat 71% of US respondents are on Facebook, with48% of US respondents using it for news.3Given the central role that social media playin public life, these platforms have become a targetfor propaganda campaigns and informationoperations. In its review of the recent US elections,Twitter found that more than 50,000 automatedaccounts were linked to Russia.4 Facebook hasrevealed that content from the Russian InternetResearch Agency has reached 126 million US citizensbefore the 2016 presidential election.5 Adding toreports about foreign influence campaigns, there isincreasing evidence of a rise in polarization in the USnews landscape in response to the 2016 election. Trustin news is strikingly divided across ideological lines,and an ecosystem of alternative news is flourishing,fueled by extremist, sensationalist, conspiratorial,masked commentary, fake news and other forms ofjunk news. At the same time, legacy publishers likethe New York Times and the Washington Post havereported an increase in subscriptions.Social media algorithms can be purposefullyused to distribute polarizing political content andmisinformation. Pariser’s claim is that filter bubbleeffects—highly personalized algorithms that selectwhat information to show in news feeds based on userpreferences and behavior—have polarized publiclife.6 Vicario et al. find that misinformation on socialmedia spreads among homogeneous and polarizedgroups.7 In January 2018, Facebook announcedchanges to its algorithm to prioritize trustworthynews, responding to ongoing public debate as towhether its algorithms promote junk content.8Consequently, social polarization is a driver—just asmuch as it may be a result—of polarized social medianews consumption patterns.In this study, we present a three-month studyof junk news and political polarization among groupsof US Twitter and Facebook users. In particular, weexamine the distribution of posts and comments onpublic pages that contain links to junk news sources,across the political spectrum in the US. We then mapthe influence of central sources of junk political newsand information that regularly publish content on hotbutton issues in the US. In particular, we considerpatterns of interaction between accounts that have (i)shared junk news, (ii) and that have engaged withusers who disseminate large amounts ofmisinformation about major political issues.SOCIAL NETWORK MAPPINGVisualizing social network data is a powerful way ofunderstanding how people share information andassociate with one another. By using selectedkeywords, seed accounts, and known links toparticular content, it is possible to construct largenetwork visualizations. The underlying networks of1
these visualizations can then be examined to findcommunities of accounts and clusters of association.These clusters of accounts and content can then becoded with political attributes based on knowledge ofaccount history, content type, association metrics andsocial interaction between accounts.These social network maps provide insightinto both social structure and flow of information. Inthis study, we use the Graphika visualization suite tomap and code accounts that are associated withprominent political accounts, topics, politicalaffiliations, and geographical areas. Social networkmapping also allows us to catalogue users andcontent, and generate both descriptive statistics andstatistical models that explain changes in networkstructure and therefore things like information flowover time.Social network maps comprise nodesrepresenting the individual accounts, which areconnected to other nodes in the map via socialrelationships. A Fruchterman–Reingold visualizationalgorithm can be used to represent the patterns ofconnection between these nodes.9 It arranges thenodes in a visualization through a centrifugal forcethat pushes nodes to the edge and a cohesive force thatpulls strongly connected nodes together.Thismapping process produces focused “segments” ofusers who share very similar and specific kinds ofcontent with each other. Segments that share somecontent with each other are aggregated into “groups”.The nodes in a network may all belong to agroup with a shared pattern of interests. These groupscan be constructed from a number of geographically,culturally, or socially similar segments. For example,segments of House Democrats, Democratic Party,Left-leaning NGOs, Liberal and anti-GOP pages, andLiberal Memes could be collectively labeled as a“Democratic Party Group”. This method ofsegmenting users, coding groups, and generatingbroad observations about association is an iterativeprocess drawing on qualitative, quantitative andcomputational methods. These are run many timesover a period of time to identify stable and consistentcommunities in a network of social media users.To create a map of segments and groups, weuse a bipartite graph to provide a structural similaritymetric between nodes in the map, which is used incombination with a clustering algorithm to segmentthe map into distinct communities. For this study,hierarchical agglomerative clustering was used toautomatically generate segments and groups fromsampled data (see online supplement for details).Different social media platforms have theirown unique attributes that are effective in identifyingcommunities that persist over time. For instance,clustering Twitter users by following and followerrelationships yields much more stable communitiesthan clustering by mention or retweet relationship.Likewise, clustering Facebook users by the “like”relationship yields similarly stable results. Therefore,for this study, we have used these attributes togenerate maps of stable clusters on Twitter andFacebook.The outputs of this clustering algorithm havebeen extensively tested by others in studies of socialmedia maps from Iran, Russia and the United2,10,11States.After clustering, the map-makingprocess uses supervised machine learning techniquesto generate labels for segments and groups from atraining set labeled by human experts. After theselabels are assigned, they are then manually verifiedand checked for accuracy and consistency.STUDY SAMPLE AND METHODFor this study, a seed of known propaganda websitesacross the political spectrum was used, drawing froma sample of 22,117,221 tweets collected during theUS election, between November 1-11, 2016. (The fullseed list is in the online supplement and available asa standalone spreadsheet.) We identified sources ofjunk news and information, based on a groundedtypology. Sources of junk news deliberately publishmisleading, deceptive or incorrect informationpurporting to be real news about politics, economicsor culture. This content includes various forms ofextremist, sensationalist, conspiratorial, maskedcommentary, fake news and other forms of junk news.For a source to be labeled as junk news it must fall inat least three of the following five domains: Professionalism: These outlets do not employthe standards and best practices of professionaljournalism. They refrain from providing clearinformation about real authors, editors,publishers and owners. They lack transparency,accountability, and do not publish corrections ondebunked information.Style: These outlets use emotionally drivenlanguage with emotive expressions, hyperbole,ad hominem attacks, misleading headlines,excessive capitalization, unsafe generalizationsand fallacies, moving images, graphic picturesand mobilizing memes.Credibility: These outlets rely on falseinformation and conspiracy theories, which theyoften employ strategically. They report withoutconsulting multiple sources and do not employfact-checking methods. Their sources are oftenuntrustworthy and their standards of newsproduction lack credibility.Bias: Reporting in these outlets is highly biasedand ideologically skewed, which is otherwisedescribed as hyper-partisan reporting. Theseoutlets frequently present opinion andcommentary essays as news.Counterfeit: These outlets mimic professionalnews media. They counterfeit fonts, brandingand stylistic content strategies. Commentary andjunk content is stylistically disguised as news,2
Table 1: Size, Coverage and Consistency of US Audience Groupson TwitterUsers Users Coverage ConsistencyN%Conservative Media1,876149520Democratic Party5764110Local News4693280Mainstream7446331Other8766672Party Politics1,34310521Progressive Movement1,1499361Republican Party8456581Resistance3,663276218Trump Support1,936149655Average1,348105410Total13,477 100.GroupConservative MediaDemocratic PartyMainstream MediaOtherLocal NewsParty PoliticsProgressiveRepublican PartyResistanceTrump SupportTable 2: Heterophily Index for US Audience Groups on TwitterConservative MediaDemocratic PartyMainstream MediaOtherLocal NewsParty PoliticsProgressiveRepublican PartyResistanceTrump 40.04.0Figure 1: US Audience Groups on TwitterSource: Authors’ calculations from data sampled 20/10/17-20/01/2018. Note: Groups are determined through network associationand our interpretation of the kinds of content these users distribute.This is a basic visualization, see online supplement for a fullvisualization.with references to news agencies, and crediblesources, and headlines written in a news tone,with bylines, date, time and location stamps.Sources of junk news were evaluated and reevaluated in a rigorously iterative coding process. Ateam of 12 trained coders, familiar with the USpolitical and media landscape, labeled sources ofnews and information based on a grounded typology.The Krippendorff’s alpha value for inter-coderreliability among three executive coders, whodeveloped the grounded typology, was 0.805. The 91sources of political news and information, which weidentified over the course of several years of researchand monitoring, produce content that includes variousforms of propaganda and ideologically nformation. We tracked how the URLs to thesewebsites were being shared over Twitter andFacebook (see online supplement for details).Specifically, we computed the coverage andconsistency scores for each group. Coverage of agroup refers to the percentage of all propagandadomains identified in our junk news sources list that agroup posted links to. The Consistency of a grouprefers to the percentage of the total of number of linksto all the propaganda domains identified in our junknews sources list, that is shared by the group. A highvalue for coverage shows that the group is sharing awide range of propaganda, while a high value forconsistency shows that the group is playing a key rolein the spreading of such propaganda. Coverage andconsistency scores were calculated from the numberof links shared from the groups to the junk newssources.FINDING: POLARIZATION AND JUNK NEWSON TWITTEROur Twitter dataset contains 13,477 Twitter userscollected during a 90-day period between October 20,2017 and January 18, 2018. To study the polarizationamong US audience groups on Twitter, we firstidentified the accounts of Democratic and Republicanparty members, at both state and national levels.Further, we identified Twitter accounts of members ofcongress from both parties. Next, we included all thefollowers of these accounts in our dataset. Weidentified a follower network of 93,711 Twitteraccounts. We then reduced this sample of Twitterusers to a set of well-connected accounts using avariant of k-core reduction (see online supplement fordetails).12 This reduced the dataset to 13,477 Twitterusers. Finally, we collected all Twitter users followedby any account in the reduced set of Twitter users, inorder to segment this set into communities of interest.We used Twitter’s REST API to collectpublicly available data for our analysis. Twitter’sREST API provides data on a) who follows whom onTwitter (100% of all data), and b) recent tweets foreach user (up to 3,200 tweets per user in reversechronological order).Twitter’s APIs give access only to publicdata and do not provide any information aboutsuspended accounts or users who set their accountsprivate. The latter limitation is not a concern here,given that 100% of Twitter users in this study havepublic accounts.133
Table 3: Size, Coverage and Consistency of US Audience Groupson FacebookUsers Users Coverage ConsistencyN%Conspiracy9469405Democratic Party1,144114012Environmental Movement9549131Hard Conservative81589158Libertarians2092344Military Guns3974454Occupy1,11410387Other Left673662Other Non-Political1,68816132Public Health733740Republican Party2412151Sustainable Farming1,14411192Women’s Rights6336131Average7657339Total10,691 HardConservativeLibertariansMilitary GunsOccupyOther LeftOther NonPoliticalPublic ghtsWomen’s RightsSustainable FarmingRepublican PartyPublic HealthOther Non-PoliticalOther LeftOccupyMilitary GunsLibertariansHard ConservativeEnvironmentalGroupDemocratic PartyTable 4: Heterophily Index for US Audience Groups on FacebookConspiracyWe were able to group our sample of 13,477user accounts into 10 groups of affiliation. The groupsemerged through network association, and byinterpretation of the kinds of content these usersdistributed and indicated as a “favorite”. Table 1identifies the main groupings of US Twitter userssampled, as labelled by our iterative machine-learningprocess and expert manual review.From Table 1, we see that the TrumpSupport Group has a coverage of 96%, indicating thatthose pages share the widest range of junk sources onTwitter. This is followed by the Conservative MediaGroup, with a coverage of 95%. We also see fromTable 1 that the Trump Support group, with aconsistency score of 55%, contributes more to thespreading of junk news, compared to all other groupsput together.Next, we next calculated a heterophily scorefor each combination of group pairings. This is ameasure of the connections between groups in anetwork, where a ratio is calculated of the actual tiesbetween two groups, compared with the expectednumber of ties between them, if all the ties in thenetwork were distributed evenly. We calculate ties forgroups on Twitter from follower accounts andaccounts followed, and Facebook ties from page likes.The natural log of the ratios is then taken along witha zero correction to create a balanced index. A highheterophily score between groups indicates moreconnections between the two groups. A highheterophily score for a group to itself indicates a highnumber of within-group connections. It is importantto note however that these scores indicate only firstorder (direct) connections between groups, and notsecond, third, or higher-order (indirect) connections.These values are shown in Table 2.From Table 2, we see that the DemocraticParty Group and the Mainstream Media Group have aheterophily index of 1.7, indicating a deep connectionbetween the two groups. A heterophily score of 1.0would indicate a perfectly neutral level of connectionbetween groups; less than 1.0 would indicate a lack ofconnection. Similarly, we see that the RepublicanParty Group shares a heterophily index of 1.6 with theConservative Media Group, indicating stronginteractions between them. The Democratic Party alsoshares a high heterophily index of 1.9 with theProgressive Movement Group, demonstratingsignificant interaction. The Mainstream Media Groupalso shares a high heterophily score with both theProgressive Movement (1.5), and the Resistance (1.2)Groups. The Republican Party and Trump Supportersshare a heterophily score of 1.4, also indicating astrong connection between them.Figure 1 is a basic visualization of the 10groups on Twitter. The size of each group isdetermined by the number of Twitter accounts thatbelong to it (see Table 1). The connections betweenthe groups in the figure are computed using theheterophily scores (see Table 2). The width of the line5.0 0.8 0.5 0.4 2.5 0.2 1.0 0.1 0.8 0.1 0.0 0.4 0.10.8 5.0 0.6 0.3 0.6 0.0 0.8 1.4 0.7 0.3 0.7 0.2 0.70.5 0.6 5.7 0.0 0.0 0.1 0.4 0.3 0.7 0.7 0.8 1.6 0.20.4 0.2 0.0 9.2 2.2 1.9 0.0 0.0 0.3 0.1 1.8 0.1 30.30.80.00.10.50.20.20.20.10.80.00.10.10.90.70.0 0.3 0.7 0.1 0.0 0.6 0.0 0.3 1.3 7.2 0.3 0.5 1.60.0 0.7 0.0 1.8 0.3 0.8 0.0 0.2 0.5 0.3 25 0.0 0.10.4 0.2 1.6 0.1 0.2 0.2 0.2 0.1 0.8 0.5 0.0 5.4 0.20.1 0.7 0.2 0.0 0.0 0.1 0.1 0.9 0.7 1.6 0.1 0.2 9.4Figure 2: US Audience Groups on FacebookSource: Authors’ calculations from data sampled 20/10/17-20/01/2018. Note: Groups are determined through network associationand our interpretation of the kinds of content these users distribute.This is a basic visualization, see online supplement for a fullvisualization.4
linking groups in the figure, represents the strength ofconnection between them.FINDING: POLARIZATION AND JUNK NEWSON PUBLIC FACEBOOK PAGESWe mapped the public Facebook pages by combining:1) harvested Facebook public page seeds frompolitical tweets shared during the US election and asnowball sample of the wider Facebook networkaround these key online interest groups; 2) a snowballsample of all the Facebook pages associated withparty Twitter accounts considered for the Twitterstudy; 3) iteration of clear US Liberal andConservative clusters from previous US politicalmaps on Facebook.This resulted in a dataset of 47,719 publicFacebook pages.
purporting to be real news about politics, economics or culture. This content includes various forms of extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news. For a source to be labeled as junk news it must fall in at least three of the following five domains:
Hindi News NDTV India 317 Hindi News TV9 Bharatvarsh 320 Hindi News News Nation 321 Hindi News INDIA NEWS NEW 322 Hindi News R Bharat 323. Hindi News News World India 324 Hindi News News 24 325 Hindi News Surya Samachar 328 Hindi News Sahara Samay 330 Hindi News Sahara Samay Rajasthan 332 . Nor
news during critical moments of public life in the US is wide, we have not seen it entirely overtake the consumption of traditional news, even amongst its most avid consumers. Since the UK’s Brexit referendum and the 2016 US presidential election, social media companies have put significant work into raising the
81 news nation news hindi 82 news 24 news hindi 83 ndtv india news hindi 84 khabar fast news hindi 85 khabrein abhi tak news hindi . 101 news x news english 102 cnn news english 103 bbc world news news english . 257 north east live news assamese 258 prag
whose signature measurements are sensitive to the polarization ofthe targets and their backgrounds. Fusion is achieved by constructing the joint statistical measures for the target's polarization states. Target polarization states are in terms ofthe intensity, percent of linear polarization, and the angle ofpolarization plane. Applications .
What polarization means? Polarization describes the alignment of a particle's spin with its momentum. Quantified using the helicity: Transversal polarization (T): the spin and momenta are (anti)-aligned (h 1, -1) Longitudinal polarization (L): spin parallel with the momenta (h 0) A caveat Polarization measurements are frame dependent
PMD (Polarization Mode Dispersion) is the differential arrival time of the different polarization components of an input light pulse, transmitted by an optical fiber. This light pulse can always be decomposed into pairs of orthogonal polarization modes. These polarization modes propagate at different speeds according to a slow and
Healthy Eating and Junk Food How is junk food harmful to body? Q3 Junk food can be harmful in many ways; some problems may appear early and others in due course of time. Most of these foods have poor nutritional quality due to high carbohydrate and fat content. Mostly they are high in sugar or salt as well. On the contrary, they are low in .
Grade 2 ELA Week of April 13-17, 2020 Day Skill Instructions Monday . There was a city park very close to their apartment. The park was really big. Maybe part of it could be turned into a park for dogs. Then Oscar s puppy would have a place to run! 4 Now Oscar needed to turn his idea into a plan. Oscar worked very hard. He wrote letters to newspapers. He wrote to the mayor about his idea for .
