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User recommendations for journalistic websites on Twitter (ICA Presentation 2012, Phoenix) Hanna Jo vom Hofe, Christian Nuernbergk, Christoph Neuberger
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User recommendations for journalistic websites on Twitter Hanna Jo vom Hofe, Christian Nuernbergk, Christoph Neuberger LMU Munich/University of Muenster
ICA 2012
May 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 news content and
news websites (self-promotion,
branding)
Provision of story ideas, sources (monitoring, filtering, reporting)
User Recommendations of news content
Social Navigation
Editorial Recommendations of news content
Automated publishing
Conversation/ Interaction
Research Design: LfM Twitter and Journalism Study
1. 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 2010
2. 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
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%)
Methodology: Content Analysis of User Recommendations
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 news content and
news websites (self-promotion,
branding)
Provision of story ideas, sources (monitoring, filtering, reporting)
User Recommendations of news content Social Filtering
Editorial Recommendations of news content
Automated Publishing
Conversation/ Interaction
19% 81%
Findings (RQ 1): Incoming Links and Reach of Top 20 News Outlets
News Outlet Investigated URL
In-Links (Tweets) in April
2010
Share of In-Links
in %
Tweets Rank (April 2010)
IVW Visits Rank (April 2010)
Rank Difference (Tweets vs.
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)
The 1st top site receives 14%, the 2nd site receives 9% and the 3rd 7% of all links (n=354.794)
M=2259,83 tweets (SD=5731,11)
results show centralization to top news sites
First quintile of the investigated sites share 82% of all tweets; Top 20 sites receive 74%
Power law-distribution
0
10.000
20.000
30.000
40.000
50.000
60.000
1 51 101 151
Spiegel Online
Welt Online
Faz.net
Focus Online
Rank
Receiv
ed
In
-lin
ks
from
tw
itte
r
Fig.: Distribution of User Recommendations 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 news content and
news websites (self-promotion,
branding)
Provision of story ideas, sources (monitoring, filtering, reporting)
User Recommendations of news content Social Filtering
Editorial Recommendations of news content
Automated Publishing
Conversation/ Interaction
Findings (RQ 2): Selection of topics in tweeted user recommendations for Top 20 news sites
Topic area of tweet/ linked article
Top 20 news sites
(n=618)
Other news sites
(n=186)
Politics 38% 26%
Economy 15% 16%
Culture 4% 10%
Sports 10% 13%
Media/Net 10% 10%
Science/Technics 8% 4%
Entertainment 5% 6%
Society/Everyday life 5% 10%
Other 6% 5%
Cramer-V=0,184, p<0,01
Topics selected for recommendation slightly differ between sites
Recommendations for popular news sites show preferences for politics, science and technology
Tweets linking to Top 20 sites comprise only a small amount of societal, everyday life and culture topics
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 Evaluations
Evaluation by subjects in Tweets
News site (n=8)
Linked article (n=7)
Actor or event in a
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 news content and
news websites (self-promotion,
branding)
Provision of story ideas, sources (monitoring, filtering, reporting)
User Recommendations of news content Social Filtering
Editorial Recommendations of news content
Automated Publishing
Conversation/ Interaction
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)
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.
Conclusion
Neuberger, 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.pdf Further information: http://en.ejo.ch/tag/twitter-and-journalism-the-influence-of-the-social-web-on-the-news
