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

Viral news: How to predict news sharing basedon article characteristics

Damian Trilling, Petro Tolochko, & Bjorn Burscher

[email protected]@damian0604

www.damiantrilling.net

Afdeling CommunicatiewetenschapUniversiteit 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

How are news shared (electronically)?

• via widgets

• through copy-pasting

Viral News Trilling, Tolochko, & Burscher

What is it?

We all know it

• via widgets

What is it?

We all know it

• via widgets

What is it?

We all know it

• via widgets

What is it?

Old theories for new questions?

News value theory

• Ostgaard (1965) and Galtung & Ruge (1965) suggested thatsome empirically determinable news factors determine thenews value of some information.

• How many and which factors ⇒ subject to debates

• Eilders (2006) argues that these factors do not only guidejournalists’ news selection, but also the selection of news bythe audience.

What is it?

News sharing and news values

• All this suggest that a user’s decision whether to share anarticle is guided by these news factors as well.

• Weber (2014) shows that at least some news factors can beused 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 activelyused for this purpose

• “virtual home, like a real one, [that] is furnished with objectsyou buy, build, or receive as gifts.” (Turkle, 1995, p. 259).These objects can be all types of ressources, especially links toother sites.

• Identities as something people are constructing continuously,as an ongoing “project of the self” (Giddens, 1991).

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 extractrelevant information

Sharing data

• (at least) 1 month time lag

• Query Facebook, Twitter, Google APIs (Python) with URLsfrom RSS-dataset

The data

Article data

Sharing data

The data

Article data

Sharing data

Descriptives

Table: Sample description

News site Description N

ad.nl popular newspaper 45 525nrc.nl quality newspaper 6 267nu.nl online-only news site 5 721parool.nl Amsterdam newspaper 24 370trouw.nl quality newspaper 19 299volkskrant.nl quality newspaper 31 500

Total N = 132, 682.

Variable N Mean SD Min Max

Dependent variablesFacebook interactions 132,682 49.25 590.91 0 79,975Twitter shares 132,682 11.95 33.79 0 4,235Google+ shares 132,682 0.45 36.97 0 13,437Independent variablesDomestic topic 132,682 0.59 0.49 0 1Geographical distance 68,290 2,926 3,419 0 18,552Cultural distance (Western=1) 68,290 0.68 0.46 0 1Negativity 132,682 2.85 0.85 1 5Conflict 132,682 0.59 0.49 0 1Human interest 132,682 0.84 0.37 0 1Positivity 132,682 1.87 0.97 1 5Press agency 132,682 0.50 0.50 0 1Topic popularity score 132,682 0.07 0.11 0 1Control variablesLength (in 1000 characters) 132,682 1.50 1.74 0 70.66Topic: defense and foreign affairs 132,682 0.14 0.34 0 1Topic: political system 132,682 0.07 0.26 0 1Topic: economic policy 132,682 0.05 0.22 0 1Topic: social affairs 132,682 0.05 0.22 0 1Topic: law and order 132,682 0.14 0.34 0 1Topic: infrastructure 132,682 0.05 0.22 0 1Topic: science 132,682 0.01 0.10 0 1Topic: culture 132,682 0.09 0.29 0 1Topic: weather 132,682 0.01 0.09 0 1Topic: sports 132,682 0.26 0.44 0 1

Analysis

Automated coding

• sentiment analysis

• supervised machine learning (based on earlier work by Bjorn)

• word counts (e.g., number of deaths)

• parsing (via XPATH) of relevant sections of the page (e.g.,author)

Models

• negative binonomial regression

Analysis

Automated coding

• sentiment analysis

• supervised machine learning (based on earlier work by Bjorn)

• 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

ResultsGeneral 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)

General impressions

Twitter

• No shares: 10%

Facebook

• No shares: 30%

General impressions

Twitter

• No shares: 10%

Facebook

• No shares: 30%

The models

ResultsThe model

FROM NEWSWORTHINESS TO SHAREWORTHINESS 31

Table 3Negative binomial regressions

Twitter Facebook

ControlsSite: 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 valuesDomestic 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 identityPositivity 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 .36Log Likelihood −422,314.200 −381,856.200θ 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 < 1indicate a negative effect, values > 1 a positive effect. ∗p < .05; ∗∗p < .01; ∗∗∗p < .001

The hypotheses and research questions

ResultsThe hypotheses and research questions

Distance

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

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

• H1c (supp.): Stories about non-Western countries ⇒ 96% ofthe shares of articles without a clear location, compared to140% which Western countries receive on Twitter (Facebook:111% vs. 167%).

Distance

• 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

Conflict

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

Conflict

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

Human interest

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

Human interest

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

Exclusiveness

• H6 (supp.): News-agency articles ⇒ only 67% of the Twittershares of own articles; even sharper decline on Facebook (to28%).

• H7 (supp. on Twitter): more frequently the specific topic ofan article is covered in the news in general ⇒ less shares.Opposite effect on Facebook

Exclusiveness

Conclusions

• Sharing is predictable

• Both news values and identity construction play a role

• Need for up-to-date theory incorporating differentperspectives:

1 user as person (identity construction, personal motivations)2 user as gatekeeper/someone in a journalist’s role3 “produsage”

[email protected]@damian0604

www.damiantrilling.net

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

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