Paper by Magdalena Wischnewski, Axel Bruns, and Tobias Keller, presented at the 2021 International Communication Association conference, 27-31 May 2021.

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Shareworthiness and motivated reasoning
in hyper-partisan news sharing behavior
on Twitter
Magdalena Wischnewski, University of Duisburg-Essen
Axel Bruns, Queensland University of Technology
Tobias Keller, gfs, Bern

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Two persepctives to hyper-partisan news
sharing
Shareworthiness factors
“Characteristics of (hyper-
partisan news) which make
articles more or less likely to be
shared on social media.”
(Trilling et al., 2017; Valenzuela et al.,
2017; Xu et al., 2020)
Users‘ motivations for sharing
(hyper-partisan) news
„Different user motivations that drive
sharing processes.“
(An et al; 2013; Marwick, 2018)

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Shareworthiness Factors
1. Proximity (Bednarek & Caple, 2017; Galtung
and Ruge, 1965; Trilling et al., 2017; Valenzuela
et al., 2017)
2. Conflict (Kim, 2015; Trilling et al., 2017;
Valenzuela et al., 2017)
3. Human interest (García-Perdomo,
Salaverría, Kilgo, & Harlow, 2018; Trilling et al.,
2017)
4. Morality (Neuman et al., 1992; Semetko &
Valkenburg, 2000; Valenzuela et al., 2017)
5. Visual Content (Goel, Anderson, Hofman,
& Watts, 2016; Xu et al., 2020)

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Motivated reasoning
Ideology-based sharing motivations
 attitude-congruent information are favored
over attitude-incongruent information
(Kunda, 1990)
Hypothesis:
“News stories that align with an individual’s attitudes are
more likely to be shared, whereas stories that do not align
with or that are neutral to an individual’s attitude are less
likely to be shared.”

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• Infowars.com’s content
distribution on Twitter
• entirely driven by third-party
accounts (accounts of
Infowars & its founder Alex
Jones were banned in 2018)
Methodology - Infowars on Twitter

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Methodology - Infowars on Twitter
• Collection of tweets that
contained an Infowars URL
through Twitter’s API
• Excluding tweets that did not fall
into our timeframe
 Only (re)tweets that were shared
within 24h after publication
• N = 5,280 tweets
• Collection of Infowars articles
through the GDELT project
(https://www.gdeltproject.org/)
• Time period: Seven days
23/09/2019 – 30/09/2019
• N = 169 articles
• Manually coding of articles into
the 5 hypothesized
shareworthiness factors
(Krippendorff’s alpha = between 0.72
and 0.8)

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1. What made it into Twitter?
• N = 168 (of 169) were shared on Twitter
2. What was more likely to be tweeted/retweeted?
• How often were articles shared on Twitter? tweet count
• What was retweeted more than what was shared in original
tweets? retweet factor*
*(tweet-count – share-count) / share-count
Results - Shareworthiness

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Results – Shareworthiness Factors
Factor B S.E. p
Proximity -1.01 0.26 <.0001***
Conflict 0.57 0.2 .003**
Human interest 0.77 0.19 <.0001***
Morality 0.33 0.21 .11
Visual -0.07 0.21 .74
Results of the negative binomial regression model for the
dependent variable tweet count (***p < .001, ** p < .01,
*p < .05).

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Results – Shareworthiness Factors
Factor B S.E. p
Proximity -1.01 0.26 <.0001***
Conflict 0.57 0.2 .003**
Human interest 0.77 0.19 <.0001***
Morality 0.33 0.21 .11
Visual -0.07 0.21 .74
Results of the negative binomial regression model for the
dependent variable tweet count (***p < .001, ** p < .01,
*p < .05).

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Results – Shareworthiness Factors
Results of the negative binomial regression model for the
dependent variable retweet factor (***p < .001, ** p <
.01, *p < .05).
Factor B S.E. p
Proximity -0.25 0.21 .22
Conflict 0.25 0.15 .11
Human interest 0.33 0.16 .04*
Morality 0.01 0.17 .95
Visual 0.003 0.16 .98

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Results – Shareworthiness Factors
Results of the negative binomial regression model for the
dependent variable retweet factor (***p < .001, ** p <
.01, *p < .05).
Factor B S.E. p
Proximity -0.25 0.21 .22
Conflict 0.25 0.15 .11
Human interest 0.33 0.16 .04*
Morality 0.01 0.17 .95
Visual 0.003 0.16 .98

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Methodology - Infowars on Twitter
• Collection of user profile
descriptions that shared an
Infowars.com URL through
Twitter API
 1,064 Twitter accounts’ profile
descriptions
• Profile description as a proxy of
user’s political interests
• Accounts without description
(2%) were omitted
• semi-automated clustering
through key-word lists of the
collected profile descriptions:
(see Spierings et al., 2018; Keller, 2020)
• Seven resulting clusters: Trump
followers, Patriots, Infowars fans,
Christians, Military, Pro-gun,
Conspiracy
• Coding of articles in the same
seven clusters (Krippendorff’s alpha =
.62 - .72)

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Results – Motivated Reasoning and sharing
Trump Christian Patriot Pro-Gun Military Conspiracy Infowars
Trump 1 0.27 0.45 0.45 0.35 0.23 0.14
Christian 1 0.09 0.13 0.15 0.07 0.02
Patriot 1 0.51 0.34 0.05 0.04
Pro-Gun 1 0.51 0.06 0.001
Military 1 0.05 0.05
Conspiracy 1 0.31
Infowars 1
Correlation matrix of Cramer’s V for Twitter profiles (higher values =
greater correlation).

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Results – Motivated Reasoning and sharing
Trump Christian Patriot Pro-Gun Military Conspiracy Infowars
Trump 1 0.06 0.02 0.05 <0.001 0.05 0.1
Christian 1 0.23 0.32 0.19 0.13 0.15
Patriot 1 0.22 0.22 0.02 0.16
Pro-Gun 1 0.03 0.13 0.1
Military 1 0.004 0.06
Conspiracy 1 0.31
Infowars 1
Correlation matrix of Cramer’s V for Infowars.com articles.
(higher values = greater correlation).

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Dependent Variable
(article)
Independent
and control
variables
(profile)
B S.E. p
Trump Articles
Trump Profile 0.27 0.07 .003***
Patriotic Profile -0.06 0.1 .55
Infowars Profile 0.15 0.17 .37
Christian
Profile
0.07 0.09 .42
Military Profile -0.005 0.12 .97
Pro-Gun Profile 0.04 0.12 .74
Conspiracy
Profile
0.26 0.15 .08
Results – Motivated Reasoning and sharing
Results of the logistic regression models with articles that reported on a particular
topic as dependent variable, profiles with interests matching that topic as independent
variable (bolded), and the remaining profiles as control variables (***p < .001, ** p <
.01, *p < .05).

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Results – Motivated Reasoning and sharing
Dependent
Variable (article)
Independent and
control variables
(profile)
B S.E. p
Patriotic Articles Patriotic Profile -0.35 0.16 .03*
Infowars Articles
Infowars Profile 0.36 0.18 .05*
Military Profile 0.32 0.14 .02*
Christian Articles
Christian Profile 0.45 0.16 .01*
Conspiracy Profile 0.51 0.25 .04*
Military Articles --- --- --- ---
Pro-Gun Articles Christian Profile 0.48 0.22 .03*
Conspiracy Articles Pro-Gun Profile -0.28 0.13 .03*
Results of the logistic regression models with articles that reported on a particular
topic as dependent variable and significant profiles with interests matching that topic as
independent variable.

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Results – Motivated Reasoning and sharing
Dependent
Variable (article)
Independent and
control variables
(profile)
B S.E. p
Patriotic Articles Patriotic Profile -0.35 0.16 .03*
Infowars Articles
Infowars Profile 0.36 0.18 .05*
Military Profile 0.32 0.14 .02*
Christian Articles
Christian Profile 0.45 0.16 .01*
Conspiracy Profile 0.51 0.25 .04*
Military Articles --- --- --- ---
Pro-Gun Articles Christian Profile 0.48 0.22 .03*
Conspiracy Articles Pro-Gun Profile -0.28 0.13 .03*
Results of the logistic regression models with articles that reported on a particular
topic as dependent variable and significant profiles with interests matching that topic as
independent variable.

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Discussion
• Hyper-partisan news sharing from the original publication
into Twitter: proximity, conflict, and human interest
• Hyper-partisan news sharing from the original publication
within Twitter: human interest
 Different factors drive sharing into Twitter compared to
sharing within Twitter
 Findings for shareworthiness depend on researchers’
definition and operationalization of shares
 Support for the motivated reasoning hypothesis except for
military, pro-gun, and conspiracy clusters

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Limitations
• Outlet dependency (Infowars)
• Social media platform (Twitter)
• Time frame (one week)
• Selection of shareworthiness factors
• Predictor variables (factors relating to the platform (Twitter)
rather than the source (Infowars) – e.g. follower count of person
sharing or hashtags used)

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Acknowledgements
Thanks to Ina Rentemeister for her support in the coding process,
as well as QUT Digital Media Research Centre staff Brenda Moon,
Ehsan Dehghan, Timothy Graham, and Daniel Angus for their
support of this work.
This work would not have been possible without the support of
European Union’s Horizon 2020 research and innovation program
under the Marie Skłodowska-Curie grant No. 823866.
THANK YOU!

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Reach out:
magdalena.wischnewski@uni-due.de
@wischnewski_m