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

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

What is it?
We all know it
How are news shared (electronically)?

What is it?
We all know it
• via widgets

What is it?
We all know it
• via widgets
• through copy-pasting

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.

What is it?
News sharing and news values

What is it?
• All this suggest that a user’s decision whether to share an
article is guided by these news factors as well.

What is it?
• 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).

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).

Data
The data

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

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

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.

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

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)

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

General impressions
Results
General impressions

General impressions
General impressions
(See also descriptives in the methods section)

General impressions
General impressions
Twitter
• Most articles <100 shares; but some >4,000
• No shares: 10%
• But: 73% receive ≤ 10 shares

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

The models
Results
The model

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

The hypotheses and research questions
Results

Distance

Distance
• H1a (supp.): Domestic issues ⇒ 129% of the expected shares
for other issues on Twitter, and even 184% on Facebook.

Distance
• H1b (supp.): Closer geographical distance ⇒ more shares

Distance
• 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%).

Tone
(rationale H2: news values; rationale H5: identity construction

Tone
• H2 (supp.): 1-point increase on 5-point negativity scale⇒
2.6% increase of Twitter shares (Facebook: 7.9%)

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%)

Conﬂict

Conﬂict
• H3 (supp.): Conﬂict angle present ⇒ 111% of the shares
(Facebook 109%).

Human interest

Human interest
• H4 (only supported for Facebook): human-interest articles ⇒
interacations up by a third

Exclusiveness

Exclusiveness
• H6 (supp.): News-agency articles ⇒ only 67% of the Twitter
shares of own articles; even sharper decline on Facebook (to
28%).

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

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”

d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net

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

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