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Viral news: How to predict news sharing based
on article characteristics
Damian Trilling, Petro Tolochko, & Bj¨orn Burscher
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net
Afdeling Communicatiewetenschap
Universiteit van Amsterdam
WAPOR, Buenos Aires, 16–19 June 2015
Towards a theory of news sharing? Methods Results Questions?
What is it?
We all know it
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
We all know it
How are news shared (electronically)?
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
We all know it
How are news shared (electronically)?
• via widgets
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
We all know it
How are news shared (electronically)?
• via widgets
• through copy-pasting
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
Old theories for new questions?
News value theory
• ¨Ostgaard (1965) and Galtung & Ruge (1965) suggested that
some empirically determinable news factors determine the
news value of some information.
• How many and which factors ⇒ subject to debates
• Eilders (2006) argues that these factors do not only guide
journalists’ news selection, but also the selection of news by
the audience.
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
Old theories for new questions?
News sharing and news values
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
Old theories for new questions?
News sharing and news values
• All this suggest that a user’s decision whether to share an
article is guided by these news factors as well.
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
Old theories for new questions?
News sharing and news values
• All this suggest that a user’s decision whether to share an
article is guided by these news factors as well.
• Weber (2014) shows that at least some news factors can be
used to predict how the audience comments on a news article
(see also Ziegele, 2014).
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
What is it?
Integrating an indentity-construction perspective
Online identities
• Sharing reflects on the individual’s identity and is actively
used for this purpose
• “virtual home, like a real one, [that] is furnished with objects
you buy, build, or receive as gifts.” (Turkle, 1995, p. 259).
These objects can be all types of ressources, especially links to
other sites.
• Identities as something people are constructing continuously,
as an ongoing “project of the self” (Giddens, 1991).
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
Data
The data
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
Data
The data
Article data
• January 2014—August 2014
• Automated query of RSS feeds 1x/hour and append to dataset
• Also: Full webpage downloaded immediately
• Later: Parsing the downloaded pages (Python) to extract
relevant information
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
Data
The data
Article data
• January 2014—August 2014
• Automated query of RSS feeds 1x/hour and append to dataset
• Also: Full webpage downloaded immediately
• Later: Parsing the downloaded pages (Python) to extract
relevant information
Sharing data
• (at least) 1 month time lag
• Query Facebook, Twitter, Google APIs (Python) with URLs
from RSS-dataset
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
Data
Descriptives
Table: Sample description
News site Description N
ad.nl popular newspaper 45 525
nrc.nl quality newspaper 6 267
nu.nl online-only news site 5 721
parool.nl Amsterdam newspaper 24 370
trouw.nl quality newspaper 19 299
volkskrant.nl quality newspaper 31 500
Total N = 132, 682.
Viral News Trilling, Tolochko, & Burscher
Variable N Mean SD Min Max
Dependent variables
Facebook interactions 132,682 49.25 590.91 0 79,975
Twitter shares 132,682 11.95 33.79 0 4,235
Google+ shares 132,682 0.45 36.97 0 13,437
Independent variables
Domestic topic 132,682 0.59 0.49 0 1
Geographical distance 68,290 2,926 3,419 0 18,552
Cultural distance (Western=1) 68,290 0.68 0.46 0 1
Negativity 132,682 2.85 0.85 1 5
Conflict 132,682 0.59 0.49 0 1
Human interest 132,682 0.84 0.37 0 1
Positivity 132,682 1.87 0.97 1 5
Press agency 132,682 0.50 0.50 0 1
Topic popularity score 132,682 0.07 0.11 0 1
Control variables
Length (in 1000 characters) 132,682 1.50 1.74 0 70.66
Topic: defense and foreign affairs 132,682 0.14 0.34 0 1
Topic: political system 132,682 0.07 0.26 0 1
Topic: economic policy 132,682 0.05 0.22 0 1
Topic: social affairs 132,682 0.05 0.22 0 1
Topic: law and order 132,682 0.14 0.34 0 1
Topic: infrastructure 132,682 0.05 0.22 0 1
Topic: science 132,682 0.01 0.10 0 1
Topic: culture 132,682 0.09 0.29 0 1
Topic: weather 132,682 0.01 0.09 0 1
Topic: sports 132,682 0.26 0.44 0 1
Towards a theory of news sharing? Methods Results Questions?
Analysis
Analysis
Automated coding
• sentiment analysis
• supervised machine learning (based on earlier work by Bj¨orn)
• word counts (e.g., number of deaths)
• parsing (via XPATH) of relevant sections of the page (e.g.,
author)
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
Analysis
Analysis
Automated coding
• sentiment analysis
• supervised machine learning (based on earlier work by Bj¨orn)
• word counts (e.g., number of deaths)
• parsing (via XPATH) of relevant sections of the page (e.g.,
author)
Models
• negative binonomial regression
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
General impressions
Results
General impressions
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
General impressions
General impressions
(See also descriptives in the methods section)
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
General impressions
General impressions
Twitter
• Most articles <100 shares; but some >4,000
• No shares: 10%
• But: 73% receive ≤ 10 shares
(See also descriptives in the methods section)
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
General impressions
General impressions
Twitter
• Most articles <100 shares; but some >4,000
• No shares: 10%
• But: 73% receive ≤ 10 shares
Facebook
• Similar, but more spread:
• No shares: 30%
• Three top articles: 48.689, 53,844 and 79,975 interactions
(See also descriptives in the methods section)
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The models
Results
The model
Viral News Trilling, Tolochko, & Burscher
Negative binomial regressions
Twitter Facebook
Controls
Site: AD 3.952∗∗∗ (3.888, 4.018) 8.469∗∗∗ (8.145, 8.804)
Site: NRC 8.191∗∗∗ (7.979, 8.409) 13.831∗∗∗ (12.923, 14.812)
Site: NU 15.872∗∗∗ (15.445, 16.312) 62.446∗∗∗ (58.326, 66.915)
Site: Trouw 1.743∗∗∗ (1.710, 1.777) 0.859∗∗∗ (0.820, 0.900)
Site: Volkskrant 2.365∗∗∗ (2.321, 2.409) 1.078∗∗ (1.029, 1.129)
Days since t0 0.999∗∗∗ (0.999, 1.000) 1.002∗∗∗ (1.002, 1.002)
Length (in 1000 characters) 1.165∗∗∗ (1.159, 1.171) 1.275∗∗∗ (1.257, 1.294)
Topic: defense and foreign affairs 0.803∗∗∗ (0.786, 0.821) 0.671∗∗∗ (0.635, 0.708)
Topic: political system 0.992 (0.968, 1.017) 0.797∗∗∗ (0.749, 0.847)
Topic: economic policy 1.007 (0.980, 1.035) 0.631∗∗∗ (0.589, 0.675)
Topic: social affairs & education 1.413∗∗∗ (1.376, 1.451) 1.436∗∗∗ (1.342, 1.538)
Topic: law and order 0.871∗∗∗ (0.853, 0.889) 0.638∗∗∗ (0.607, 0.671)
Topic: infrastructure 1.101∗∗∗ (1.071, 1.131) 0.953 (0.891, 1.020)
Topic: science & technology 1.129∗∗∗ (1.070, 1.191) 2.037∗∗∗ (1.786, 2.334)
Topic: culture & entertainment 1.136∗∗∗ (1.110, 1.162) 1.525∗∗∗ (1.439, 1.616)
Topic: weather & disasters 0.787∗∗∗ (0.741, 0.836) 1.607∗∗∗ (1.394, 1.862)
Topic: sports 0.636∗∗∗ (0.623, 0.648) 0.357∗∗∗ (0.341, 0.374)
Shareworthiness based on news values
Domestic topic 1.288∗∗∗ (1.271, 1.305) 1.837∗∗∗ (1.779, 1.896)
Geographical distance: 0km 1.141∗∗∗ (1.097, 1.187) 0.958 (0.870, 1.054)
Geographical distance: <500km 0.869∗∗∗ (0.831, 0.908) 0.526∗∗∗ (0.472, 0.585)
Geographical distance: <1,000km 0.875∗∗∗ (0.837, 0.913) 0.568∗∗∗ (0.511, 0.632)
Geographical distance: <2,000km 0.906∗∗∗ (0.870, 0.944) 0.712∗∗∗ (0.644, 0.786)
Geographical distance: <5,000km 0.953∗ (0.917, 0.990) 0.731∗∗∗ (0.664, 0.803)
Geographical distance: <10,000km 0.942∗∗ (0.906, 0.979) 0.709∗∗∗ (0.644, 0.779)
Cultural distance: Non-Western country 0.956∗ (0.921, 0.992) 1.108∗ (1.010, 1.218)
Cultural distance: Western country 1.140∗∗∗ (1.098, 1.183) 1.665∗∗∗ (1.522, 1.824)
Negativity 1.026∗∗∗ (1.019, 1.033) 1.079∗∗∗ (1.061, 1.097)
Conflict 1.105∗∗∗ (1.092, 1.119) 1.093∗∗∗ (1.061, 1.125)
Human interest 1.002 (0.988, 1.017) 1.330∗∗∗ (1.281, 1.379)
Shareworthiness based on online identity
Positivity 1.043∗∗∗ (1.037, 1.049) 1.164∗∗∗ (1.146, 1.182)
Press-agency 0.666∗∗∗ (0.657, 0.675) 0.276∗∗∗ (0.267, 0.285)
topic popularity score 0.740∗∗∗ (0.705, 0.778) 2.142∗∗∗ (1.884, 2.439)
Nagelkerke Pseudo-R2 .56 .36
Log Likelihood −422,314.200 −381,856.200
θ 1.307∗∗∗ (0.006) 0.188∗∗∗ (0.001)
AIC 844,694.400 763,778.400
Note. N = 132, 682. Incidence rate ratios (IRRs) with confidence intervals. Values < 1
indicate a negative effect, values > 1 a positive effect. ∗p < .05; ∗∗p < .01; ∗∗∗p < .001
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Results
The hypotheses and research questions
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Distance
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Distance
• H1a (supp.): Domestic issues ⇒ 129% of the expected shares
for other issues on Twitter, and even 184% on Facebook.
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Distance
• H1a (supp.): Domestic issues ⇒ 129% of the expected shares
for other issues on Twitter, and even 184% on Facebook.
• H1b (supp.): Closer geographical distance ⇒ more shares
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Distance
• H1a (supp.): Domestic issues ⇒ 129% of the expected shares
for other issues on Twitter, and even 184% on Facebook.
• H1b (supp.): Closer geographical distance ⇒ more shares
• H1c (supp.): Stories about non-Western countries ⇒ 96% of
the shares of articles without a clear location, compared to
140% which Western countries receive on Twitter (Facebook:
111% vs. 167%).
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Tone
(rationale H2: news values; rationale H5: identity construction
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Tone
• H2 (supp.): 1-point increase on 5-point negativity scale⇒
2.6% increase of Twitter shares (Facebook: 7.9%)
(rationale H2: news values; rationale H5: identity construction
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Tone
• H2 (supp.): 1-point increase on 5-point negativity scale⇒
2.6% increase of Twitter shares (Facebook: 7.9%)
• H5 (supp.): 1-point increase on positivity scale ⇒ 4.3%
increase on Twitter (Facebook: 16.4%)
(rationale H2: news values; rationale H5: identity construction
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Conflict
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Conflict
• H3 (supp.): Conflict angle present ⇒ 111% of the shares
(Facebook 109%).
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Human interest
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Human interest
• H4 (only supported for Facebook): human-interest articles ⇒
interacations up by a third
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Exclusiveness
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Exclusiveness
• H6 (supp.): News-agency articles ⇒ only 67% of the Twitter
shares of own articles; even sharper decline on Facebook (to
28%).
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Exclusiveness
• H6 (supp.): News-agency articles ⇒ only 67% of the Twitter
shares of own articles; even sharper decline on Facebook (to
28%).
• H7 (supp. on Twitter): more frequently the specific topic of
an article is covered in the news in general ⇒ less shares.
Opposite effect on Facebook
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
The hypotheses and research questions
Conclusions
• Sharing is predictable
• Both news values and identity construction play a role
• Need for up-to-date theory incorporating different
perspectives:
1 user as person (identity construction, personal motivations)
2 user as gatekeeper/someone in a journalist’s role
3 “produsage”
Viral News Trilling, Tolochko, & Burscher
Towards a theory of news sharing? Methods Results Questions?
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net
Viral News Trilling, Tolochko, & Burscher

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Viral news: How to predict news sharing based on article characteristics

  • 1. Viral news: How to predict news sharing based on article characteristics Damian Trilling, Petro Tolochko, & Bj¨orn Burscher d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Afdeling Communicatiewetenschap Universiteit van Amsterdam WAPOR, Buenos Aires, 16–19 June 2015
  • 2. Towards a theory of news sharing? Methods Results Questions? What is it? We all know it Viral News Trilling, Tolochko, & Burscher
  • 3. Towards a theory of news sharing? Methods Results Questions? What is it? We all know it How are news shared (electronically)? Viral News Trilling, Tolochko, & Burscher
  • 4. Towards a theory of news sharing? Methods Results Questions? What is it? We all know it How are news shared (electronically)? • via widgets Viral News Trilling, Tolochko, & Burscher
  • 5. Towards a theory of news sharing? Methods Results Questions? What is it? We all know it How are news shared (electronically)? • via widgets • through copy-pasting Viral News Trilling, Tolochko, & Burscher
  • 6. Towards a theory of news sharing? Methods Results Questions? What is it? Old theories for new questions? News value theory • ¨Ostgaard (1965) and Galtung & Ruge (1965) suggested that some empirically determinable news factors determine the news value of some information. • How many and which factors ⇒ subject to debates • Eilders (2006) argues that these factors do not only guide journalists’ news selection, but also the selection of news by the audience. Viral News Trilling, Tolochko, & Burscher
  • 7. Towards a theory of news sharing? Methods Results Questions? What is it? Old theories for new questions? News sharing and news values Viral News Trilling, Tolochko, & Burscher
  • 8. Towards a theory of news sharing? Methods Results Questions? What is it? Old theories for new questions? News sharing and news values • All this suggest that a user’s decision whether to share an article is guided by these news factors as well. Viral News Trilling, Tolochko, & Burscher
  • 9. Towards a theory of news sharing? Methods Results Questions? What is it? Old theories for new questions? News sharing and news values • All this suggest that a user’s decision whether to share an article is guided by these news factors as well. • Weber (2014) shows that at least some news factors can be used to predict how the audience comments on a news article (see also Ziegele, 2014). Viral News Trilling, Tolochko, & Burscher
  • 10. Towards a theory of news sharing? Methods Results Questions? What is it? Integrating an indentity-construction perspective Online identities • Sharing reflects on the individual’s identity and is actively used for this purpose • “virtual home, like a real one, [that] is furnished with objects you buy, build, or receive as gifts.” (Turkle, 1995, p. 259). These objects can be all types of ressources, especially links to other sites. • Identities as something people are constructing continuously, as an ongoing “project of the self” (Giddens, 1991). Viral News Trilling, Tolochko, & Burscher
  • 11. Towards a theory of news sharing? Methods Results Questions? Data The data Viral News Trilling, Tolochko, & Burscher
  • 12. Towards a theory of news sharing? Methods Results Questions? Data The data Article data • January 2014—August 2014 • Automated query of RSS feeds 1x/hour and append to dataset • Also: Full webpage downloaded immediately • Later: Parsing the downloaded pages (Python) to extract relevant information Viral News Trilling, Tolochko, & Burscher
  • 13. Towards a theory of news sharing? Methods Results Questions? Data The data Article data • January 2014—August 2014 • Automated query of RSS feeds 1x/hour and append to dataset • Also: Full webpage downloaded immediately • Later: Parsing the downloaded pages (Python) to extract relevant information Sharing data • (at least) 1 month time lag • Query Facebook, Twitter, Google APIs (Python) with URLs from RSS-dataset Viral News Trilling, Tolochko, & Burscher
  • 14. Towards a theory of news sharing? Methods Results Questions? Data Descriptives Table: Sample description News site Description N ad.nl popular newspaper 45 525 nrc.nl quality newspaper 6 267 nu.nl online-only news site 5 721 parool.nl Amsterdam newspaper 24 370 trouw.nl quality newspaper 19 299 volkskrant.nl quality newspaper 31 500 Total N = 132, 682. Viral News Trilling, Tolochko, & Burscher
  • 15. Variable N Mean SD Min Max Dependent variables Facebook interactions 132,682 49.25 590.91 0 79,975 Twitter shares 132,682 11.95 33.79 0 4,235 Google+ shares 132,682 0.45 36.97 0 13,437 Independent variables Domestic topic 132,682 0.59 0.49 0 1 Geographical distance 68,290 2,926 3,419 0 18,552 Cultural distance (Western=1) 68,290 0.68 0.46 0 1 Negativity 132,682 2.85 0.85 1 5 Conflict 132,682 0.59 0.49 0 1 Human interest 132,682 0.84 0.37 0 1 Positivity 132,682 1.87 0.97 1 5 Press agency 132,682 0.50 0.50 0 1 Topic popularity score 132,682 0.07 0.11 0 1 Control variables Length (in 1000 characters) 132,682 1.50 1.74 0 70.66 Topic: defense and foreign affairs 132,682 0.14 0.34 0 1 Topic: political system 132,682 0.07 0.26 0 1 Topic: economic policy 132,682 0.05 0.22 0 1 Topic: social affairs 132,682 0.05 0.22 0 1 Topic: law and order 132,682 0.14 0.34 0 1 Topic: infrastructure 132,682 0.05 0.22 0 1 Topic: science 132,682 0.01 0.10 0 1 Topic: culture 132,682 0.09 0.29 0 1 Topic: weather 132,682 0.01 0.09 0 1 Topic: sports 132,682 0.26 0.44 0 1
  • 16. Towards a theory of news sharing? Methods Results Questions? Analysis Analysis Automated coding • sentiment analysis • supervised machine learning (based on earlier work by Bj¨orn) • word counts (e.g., number of deaths) • parsing (via XPATH) of relevant sections of the page (e.g., author) Viral News Trilling, Tolochko, & Burscher
  • 17. Towards a theory of news sharing? Methods Results Questions? Analysis Analysis Automated coding • sentiment analysis • supervised machine learning (based on earlier work by Bj¨orn) • word counts (e.g., number of deaths) • parsing (via XPATH) of relevant sections of the page (e.g., author) Models • negative binonomial regression Viral News Trilling, Tolochko, & Burscher
  • 18. Towards a theory of news sharing? Methods Results Questions? General impressions Results General impressions Viral News Trilling, Tolochko, & Burscher
  • 19. Towards a theory of news sharing? Methods Results Questions? General impressions General impressions (See also descriptives in the methods section) Viral News Trilling, Tolochko, & Burscher
  • 20. Towards a theory of news sharing? Methods Results Questions? General impressions General impressions Twitter • Most articles <100 shares; but some >4,000 • No shares: 10% • But: 73% receive ≤ 10 shares (See also descriptives in the methods section) Viral News Trilling, Tolochko, & Burscher
  • 21. Towards a theory of news sharing? Methods Results Questions? General impressions General impressions Twitter • Most articles <100 shares; but some >4,000 • No shares: 10% • But: 73% receive ≤ 10 shares Facebook • Similar, but more spread: • No shares: 30% • Three top articles: 48.689, 53,844 and 79,975 interactions (See also descriptives in the methods section) Viral News Trilling, Tolochko, & Burscher
  • 22. Towards a theory of news sharing? Methods Results Questions? The models Results The model Viral News Trilling, Tolochko, & Burscher
  • 23. Negative binomial regressions Twitter Facebook Controls Site: AD 3.952∗∗∗ (3.888, 4.018) 8.469∗∗∗ (8.145, 8.804) Site: NRC 8.191∗∗∗ (7.979, 8.409) 13.831∗∗∗ (12.923, 14.812) Site: NU 15.872∗∗∗ (15.445, 16.312) 62.446∗∗∗ (58.326, 66.915) Site: Trouw 1.743∗∗∗ (1.710, 1.777) 0.859∗∗∗ (0.820, 0.900) Site: Volkskrant 2.365∗∗∗ (2.321, 2.409) 1.078∗∗ (1.029, 1.129) Days since t0 0.999∗∗∗ (0.999, 1.000) 1.002∗∗∗ (1.002, 1.002) Length (in 1000 characters) 1.165∗∗∗ (1.159, 1.171) 1.275∗∗∗ (1.257, 1.294) Topic: defense and foreign affairs 0.803∗∗∗ (0.786, 0.821) 0.671∗∗∗ (0.635, 0.708) Topic: political system 0.992 (0.968, 1.017) 0.797∗∗∗ (0.749, 0.847) Topic: economic policy 1.007 (0.980, 1.035) 0.631∗∗∗ (0.589, 0.675) Topic: social affairs & education 1.413∗∗∗ (1.376, 1.451) 1.436∗∗∗ (1.342, 1.538) Topic: law and order 0.871∗∗∗ (0.853, 0.889) 0.638∗∗∗ (0.607, 0.671) Topic: infrastructure 1.101∗∗∗ (1.071, 1.131) 0.953 (0.891, 1.020) Topic: science & technology 1.129∗∗∗ (1.070, 1.191) 2.037∗∗∗ (1.786, 2.334) Topic: culture & entertainment 1.136∗∗∗ (1.110, 1.162) 1.525∗∗∗ (1.439, 1.616) Topic: weather & disasters 0.787∗∗∗ (0.741, 0.836) 1.607∗∗∗ (1.394, 1.862) Topic: sports 0.636∗∗∗ (0.623, 0.648) 0.357∗∗∗ (0.341, 0.374) Shareworthiness based on news values Domestic topic 1.288∗∗∗ (1.271, 1.305) 1.837∗∗∗ (1.779, 1.896) Geographical distance: 0km 1.141∗∗∗ (1.097, 1.187) 0.958 (0.870, 1.054) Geographical distance: <500km 0.869∗∗∗ (0.831, 0.908) 0.526∗∗∗ (0.472, 0.585) Geographical distance: <1,000km 0.875∗∗∗ (0.837, 0.913) 0.568∗∗∗ (0.511, 0.632) Geographical distance: <2,000km 0.906∗∗∗ (0.870, 0.944) 0.712∗∗∗ (0.644, 0.786) Geographical distance: <5,000km 0.953∗ (0.917, 0.990) 0.731∗∗∗ (0.664, 0.803) Geographical distance: <10,000km 0.942∗∗ (0.906, 0.979) 0.709∗∗∗ (0.644, 0.779) Cultural distance: Non-Western country 0.956∗ (0.921, 0.992) 1.108∗ (1.010, 1.218) Cultural distance: Western country 1.140∗∗∗ (1.098, 1.183) 1.665∗∗∗ (1.522, 1.824) Negativity 1.026∗∗∗ (1.019, 1.033) 1.079∗∗∗ (1.061, 1.097) Conflict 1.105∗∗∗ (1.092, 1.119) 1.093∗∗∗ (1.061, 1.125) Human interest 1.002 (0.988, 1.017) 1.330∗∗∗ (1.281, 1.379) Shareworthiness based on online identity Positivity 1.043∗∗∗ (1.037, 1.049) 1.164∗∗∗ (1.146, 1.182) Press-agency 0.666∗∗∗ (0.657, 0.675) 0.276∗∗∗ (0.267, 0.285) topic popularity score 0.740∗∗∗ (0.705, 0.778) 2.142∗∗∗ (1.884, 2.439) Nagelkerke Pseudo-R2 .56 .36 Log Likelihood −422,314.200 −381,856.200 θ 1.307∗∗∗ (0.006) 0.188∗∗∗ (0.001) AIC 844,694.400 763,778.400 Note. N = 132, 682. Incidence rate ratios (IRRs) with confidence intervals. Values < 1 indicate a negative effect, values > 1 a positive effect. ∗p < .05; ∗∗p < .01; ∗∗∗p < .001
  • 24. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Results The hypotheses and research questions Viral News Trilling, Tolochko, & Burscher
  • 25. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Distance Viral News Trilling, Tolochko, & Burscher
  • 26. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Distance • H1a (supp.): Domestic issues ⇒ 129% of the expected shares for other issues on Twitter, and even 184% on Facebook. Viral News Trilling, Tolochko, & Burscher
  • 27. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Distance • H1a (supp.): Domestic issues ⇒ 129% of the expected shares for other issues on Twitter, and even 184% on Facebook. • H1b (supp.): Closer geographical distance ⇒ more shares Viral News Trilling, Tolochko, & Burscher
  • 28. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Distance • H1a (supp.): Domestic issues ⇒ 129% of the expected shares for other issues on Twitter, and even 184% on Facebook. • H1b (supp.): Closer geographical distance ⇒ more shares • H1c (supp.): Stories about non-Western countries ⇒ 96% of the shares of articles without a clear location, compared to 140% which Western countries receive on Twitter (Facebook: 111% vs. 167%). Viral News Trilling, Tolochko, & Burscher
  • 29. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Tone (rationale H2: news values; rationale H5: identity construction Viral News Trilling, Tolochko, & Burscher
  • 30. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Tone • H2 (supp.): 1-point increase on 5-point negativity scale⇒ 2.6% increase of Twitter shares (Facebook: 7.9%) (rationale H2: news values; rationale H5: identity construction Viral News Trilling, Tolochko, & Burscher
  • 31. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Tone • H2 (supp.): 1-point increase on 5-point negativity scale⇒ 2.6% increase of Twitter shares (Facebook: 7.9%) • H5 (supp.): 1-point increase on positivity scale ⇒ 4.3% increase on Twitter (Facebook: 16.4%) (rationale H2: news values; rationale H5: identity construction Viral News Trilling, Tolochko, & Burscher
  • 32. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Conflict Viral News Trilling, Tolochko, & Burscher
  • 33. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Conflict • H3 (supp.): Conflict angle present ⇒ 111% of the shares (Facebook 109%). Viral News Trilling, Tolochko, & Burscher
  • 34. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Human interest Viral News Trilling, Tolochko, & Burscher
  • 35. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Human interest • H4 (only supported for Facebook): human-interest articles ⇒ interacations up by a third Viral News Trilling, Tolochko, & Burscher
  • 36. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Exclusiveness Viral News Trilling, Tolochko, & Burscher
  • 37. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Exclusiveness • H6 (supp.): News-agency articles ⇒ only 67% of the Twitter shares of own articles; even sharper decline on Facebook (to 28%). Viral News Trilling, Tolochko, & Burscher
  • 38. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Exclusiveness • H6 (supp.): News-agency articles ⇒ only 67% of the Twitter shares of own articles; even sharper decline on Facebook (to 28%). • H7 (supp. on Twitter): more frequently the specific topic of an article is covered in the news in general ⇒ less shares. Opposite effect on Facebook Viral News Trilling, Tolochko, & Burscher
  • 39. Towards a theory of news sharing? Methods Results Questions? The hypotheses and research questions Conclusions • Sharing is predictable • Both news values and identity construction play a role • Need for up-to-date theory incorporating different perspectives: 1 user as person (identity construction, personal motivations) 2 user as gatekeeper/someone in a journalist’s role 3 “produsage” Viral News Trilling, Tolochko, & Burscher
  • 40. Towards a theory of news sharing? Methods Results Questions? d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Viral News Trilling, Tolochko, & Burscher