ICA 2012: User recommendations for journalistic websites on Twitter
User recommendations for journalisticwebsites on TwitterHanna Jo vom Hofe, Christian Nuernbergk, ChristophNeubergerLMU Munich/University of MuensterICA 2012May 26th 2012
Agenda Introduction: Complementary Relations between Twitter and Journalism Research Design and Methodology Findings Conclusion
Complementary Relations between Twitter and Journalism Professional Journalism Promotion of Provision of news content and story ideas, news websites sources (self-promotion, (monitoring, filtering, branding) reporting) Twitter Conversation/ Interaction Editorial Recommendations User Recommendations of news content of news content Automated publishing Social Navigation
Research Design: LfM Twitter and Journalism Study1. German Newsroom Survey 2010: media types: daily and weekly newspapers, general-interest magazines, supra-regional/national TV/radio, Internet-only news sites identification of 157 media outlets/news providers respondents: editors-in-chief/members of editorial departments (response rate: 45%, n=70) in May/June 20102. Content Analysis of User Recommendations Detection of all tweets with links to news sites analyzed in the parallel newsroom survey Monitoring tool: Backtweets.com web application
Methodology: Content Analysis of User Recommendations 354.794 tweets contained a link to one of the 157 sites in April 2010 links pointed to either website domains or specific articles for each site, the number of in-links was calculated Systematic sampling and analysis of 1000 tweets sample inclusion of news sites proportional to their share of in- links inclusion of every 1st and 5th hit on each result page for a specific link search on backtweets.com (27th may 2010) until the previously calculated proportional share was reached for a site
Methodology: Content Analysis of User Recommendations quantitative analysis of topic area and link type in tweets destination/reference type (e. g. website, specific article or actors and events adressed in an article) evaluation (positive, negative or balanced valence) coding of n=993 tweets by three coders inter-coder reliability: 0.94 (Holsti’s coefficient) units were fully coded if tweets were not published by official editorial accounts on Twitter (no editorial self-promotion) exclusion of 186 editorial tweets (19%)
RQ 1: What structural patterns do user recommendations exhibit on Twitter? How centralized is the distribution of user recommendations to single news sites? Do user recommendations reflect the prominence of a news site on the web (in terms of reach)?RQ 2: What sort of news gathering, filtering and evaluations are made transparent through user recommendations? What kind of topics on news sites are selected for recommendations? To what extent do user recommendations attach comments to links to news sites and/or their articles?
RQ 1: What structural patterns do user recommendations exhibit on Twitter? Professional Journalism Promotion of Provision of news content and story ideas, news websites sources (self-promotion, (monitoring, filtering, branding) reporting) Twitter Conversation/ Interaction Editorial Recommendations User Recommendations 19% of news content of news content 81% Automated Publishing Social Filtering
Findings (RQ 1): Incoming Links and Reach of Top 20 News Outlets In-Links Tweets IVW Visits Rank Share of (Tweets) Rank Rank Difference News Outlet Investigated URL In-Links in April (April (April (Tweets vs. in % 2010 2010) 2010) Visits) Spiegel Online spiegel.de/ 48.794 14 1 2 1 Welt Online welt.de/ 32.792 9 2 4 2 faz.net faz.net/ 23.658 7 3 8 5 Focus Online focus.de/ 17.638 5 4 5 1 tagesschau.de tagesschau.de/ 15.905 5 5 n. a. n. a. bild.de bild.de/ 14.433 4 6 1 -4 Yahoo! Deutschland de.news.yahoo.com/ 13.681 4 7 n. a. n. a. Handelsblatt handelsblatt.com/ 12.109 3 8 16 10 Zeit Online zeit.de/ 11.374 3 9 12 5 stern.de stern.de/ 10.278 3 10 10 2 sueddeutsche.de sueddeutsche.de/ 10.220 3 11 6 -3 Financial Times Deutschl. ftd.de/ 9.631 3 12 15 5 n-tv n-tv.de/ 8.322 2 13 7 -4 Der Westen derwesten.de/ 7.328 2 14 18 6 Berliner Morgenpost morgenpost.de/ 5.757 2 15 30 17 taz.de taz.de/ 4.386 1 16 24 10 manager magazin manager-magazin.de/ 3.814 1 17 20 5 Der Tagesspiegel tagesspiegel.de/ 3.675 1 18 22 6 Abacho.de abacho.de/ 3.569 1 19 38 21 Saarbrücker Zeitung saarbruecker-zeitung.de/ 3.506 1 20 46 28
Findings (RQ1) 60.000 The 1st top site receives 14%, the 2nd siteReceived In-links from twitter 50.000 Spiegel Online receives 9% and the 3rd 7% of all links 40.000 (n=354.794) Welt Online 30.000 M=2259,83 tweets (SD=5731,11) Faz.net 20.000 results show centralization to top news sites Focus Online 10.000 First quintile of the investigated sites share 0 1 51 101 151 82% of all tweets; Top 20 sites receive 74% Rank Fig.: Distribution of User Recommendations Power law-distribution per News site IVW visits ranking and tweet in-links ranking show a robust correlation (Spearmans rs=0,736, p<0,01, n=115)
RQ 2: What sorts of news gathering, filtering and evaluations are made transparent through user recommendations? Professional Journalism Promotion of Provision of news content and story ideas, news websites sources (self-promotion, (monitoring, filtering, branding) reporting) Twitter Conversation/ Interaction Editorial Recommendations User Recommendations of news content of news content Automated Publishing Social Filtering
Findings (RQ 2): Selection of topics in tweeted user recommendations for Top 20 news sitesTopic area Top 20 news Other news Topics selected forof tweet/ sites sites recommendation slightly differlinked article (n=618) (n=186) between sitesPolitics 38% 26%Economy 15% 16% Recommendations for popularCulture 4% 10% news sites show preferencesSports 10% 13% for politics, science and technologyMedia/Net 10% 10%Science/Technics 8% 4% Tweets linking to Top 20 sitesEntertainment 5% 6% comprise only a small amountSociety/Everyday life 5% 10% of societal, everyday life andOther 6% 5% culture topicsCramer-V=0,184, p<0,01
Findings (RQ 2): Additional Comments and Evaluations Tweets with links to news sites mostly ignore value judgments: 90% (n=807) of all counted links were not embedded into an evaluative context. Only 10% of all recommendations attach comments directed to the news site, the article of interest, or an event or actors cited/described in the linked article. Tweeted value judgments are mainly negative (53%, n=81) Balanced evaluations remained seldom (9%, n=81)
Findings (RQ 2): Additional Comments and EvaluationsEvaluation by subjects in Tweets Actor or News site Linked article event in a (n=8) (n=7) linked article (n=65) Negative 25 0 61 Balanced 25 0 8 Positive 50 100 31 Cramer-V=0,362, p<0,01 User recommendations seldomly include evaluations of a linked news site or an article in general. More often, tweets discuss or rate actors and events addressed in the linked articles.
Additional Survey Findings Professional Journalism Promotion of Provision of news content and story ideas, news websites sources (self-promotion, (monitoring, filtering, branding) reporting) Twitter Conversation/ Interaction Editorial Recommendations User Recommendations of news content of news content Automated Publishing Social Filtering
Survey Findings: Recommendations from the editors’ view According to the survey results from 2010, staff members notice responses on their own reporting on Twitter Monitoring: 60% of Top 20 sites members (n=10) and 48% (n=48) of other interviewees search for user responses on their reporting Recommendations and site traffic: Almost all respondents (93%, n=42) estimated traffic amount delivered by Twitter to their news site <10% In general, survey findings show that Twitter and other social media are not regularly employed in terms of journalistic research Limited impact compared to other tools and ways of computer- assisted reporting (e. g. search-engines, databases) Staff members most often apply Twitter to check the general opinion climate (59%, n=58)
Conclusion User recommendations for news sites indicate concentration tendencies and partially reflect the reach of various news sites. Only a small subset of websites receives a substantial share of traffic through Twitter recommendations. In comparison to other topics, political issues strongly dominate – recommendation filter and user-led news gathering is biased (in favor of politics and web news). Comments are rarely attached to tweets linking to news sites. Recommendations with comments contain value judgments mainly on events or actors in linked articles.
email@example.com firstname.lastname@example.org@uni-muenster.deNeuberger, Christoph/vom Hofe, Hanna Jo/Nuernbergk, Christian (2010):Twitter und Journalismus. Der Einfluss des "Social Web" auf die Nachrichten.Düsseldorf: Landesanstalt für Medien Nordrhein-Westfalen (LfM) (=LfM-Dokumentation Nr. 38).http://www.lfm-nrw.de/fileadmin/lfm-nrw/Publikationen-Download/LfM_Doku38_Twitter_Online.pdfFurther information:http://en.ejo.ch/tag/twitter-and-journalism-the-influence-of-the-social-web-on-the-news