What Makes News Sharable on Social Media?
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What Makes News Sharable on Social Media?
Cathy Xi Chen1, Gordon Pennycook2,3, David G. Rand1,4,5
1 Sloan School of Management, Massachusetts Institute of Technology, 2 Hill/Levene Schools of Business, University of Regina,
3 Department of Psychology, University of Regina, 4 Institute for Data, Systems, and Society, Massachusetts Institute of
Technology, 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
With the rise of social media, everyone has the potential to be both a consumer and producer
of online content. As a result, the role that word of mouth plays in news consumption has
been dramatically increased. Although one might assume that consumers share news because
they believe it to be true, widespread concerns about the spread of misinformation suggest
that truthfulness may actually not be a dominant driver of sharing online. Across two studies
with 5,000 participants, we investigate what makes news sharable on social media. We find
that sharing is positively predicted by two separate factors. One factor does involve the
headline’s perceived accuracy, as well as its familiarity. The second, however, involves the
headline’s perceived importance and emotional evocativeness. This second factor is
negatively associated with the headline’s objective veracity, and less decision weight is put
on the second factor by subjects with more cognitive reflection and political knowledge, and
by subjects who are less politically conservative. These findings have important implications
for news publishers, social media platforms, and society at large.
Keywords: social media; news sharing; word of mouth; online consumer behavior;
misinformation
Posted 7/9/2021
This working paper has not yet been peer reviewed
Introduction
The advent of social media has drastically changed the way that consumers share information
(e.g., Lamberton & Stephen, 2016). One area where this change is particularly salient is news,
with more people than ever getting their news from social media (e.g., Pew Research Center,
2017). Traditionally, a small number of news outlets broadcast news to millions of passive
consumers. On social media, however, everyone has the potential to be both a consumer and
producer of content. Hence, the rise of social media dramatically increases the role word of
mouth communication plays in news consumption. Among many other things, this dynamic has
been argued to have fueled the proliferation of misinformation and “fake news” (Lazer et al.,
2018), which has important consequences for a wide range of consumer behaviors, and major
economic impacts. Therefore, in the current work, we use the perspective of word of mouth to
investigate how consumers decide what true versus false or misleading news to share on social
media.A substantial body of research on word of mouth has examined why consumers talk about certain topic and content with others (e.g., Berger & Schwartz, 2011; Toubia & Stephen, 2013; Chen & Berger, 2013; De Angelis, et al., 2012; see Berger, 2014, for a review). In addition, research has also investigated how features of the content itself affects sharing. For example, content that elicits more positive feelings (such as interesting or humorous) is more likely to spread (e.g., Bakshy, et al., 2011; Warren & Berger, 2011). Beyond simply evoking positive feelings, emotional content that specifically evokes high arousal (such as awe, anger, or anxiety) is more likely to be shared (e.g., Berger, 2011; Berger & Milkman, 2012); and sentiment volatility is suggested to be important in making cultural products more successful (Berger, Kim, & Meyer, 2021). Furthermore, content that is seen as useful and informative is also more likely to be shared (e.g., Berger & Milkman, 2012; Heath, Bell, & Sternberg, 2001). In the current work, we shed new light on word of mouth by investigating the social media sharing of news that varies in objective truth. Although one might naively assume that consumers only share news that they believe to be true, in fact empirical evidence suggests that false content may spread as much (Grinberg et al., 2019) or more (Vosoughi, Roy, & Aral, 2018) than similar true content. Experimental evidence also demonstrates a disconnect between accuracy judgments and sharing intentions: despite saying that accuracy is important to them, people sometimes (even often) share news that they could identify as being false if they considered its accuracy (Pennycook, Epstein, et al., 2021; Epstein, et al., 2021; Pennycook, McPhetres, et al., 2020). A natural question raised by these observations, then, is: If accuracy is not a primary driver of choices about what news content to share, then what does predict sharing of accurate vs inaccurate news? From prior work, we can assemble a set of hypotheses. The work reviewed above suggests that emotional evocativeness, informational value, and usefulness/importance may predict news sharing intentions (e.g., Berger, 2014). Furthermore, people are more likely to share news that aligns with their political ideology, in both survey experiments (Pennycook, Cannon, & Rand, 2018; Pennycook & Rand, 2019; Pennycook, Epstein, et al., 2021) and panel studies on social media (Grinberg et al., 2019). Another line of work suggests that familiarity may influence sharing, with Twitter data finding that novel content is more likely to be shared (Vosoughi, Roy, & Aral, 2018) but survey data suggesting that familiarity is positively associated with sharing (Pennycook & Rand, 2020). Therefore, in the current work, we examine the relationship between sharing intentions and each of these content dimensions, as well as perceived accuracy. We also examine individual differences in the relationship between these content dimensions and sharing. For example, prior work has found that people who engage in less cognitive reflection (i.e., analytic thinking) have been found to share more false content in survey experiments (Pennycook & Rand, 2019; Pennycook & Rand, 2020; Bronstein et al. 2019; Ross, Rand, & Pennycook, 2021) and share more news from less reliable sources on Twitter (Mosleh, et al., 2021; although see Osmundsen et al., 2021). Furthermore, studies of Twitter and Facebook have found that Republicans share more false content than Democrats (Grinberg et al., 2019; Guess,
Nagler, & Tucker, 2019). Thus, we ask how these and other individual characteristics predict the
weight put on different content dimensions when deciding what to share.
Methods
Participants
Study 1 was conducted during June 2019. For robustness, we recruited American participants
from two different online subject recruitment platforms, a convenience sample from Amazon’s
Mechanical Turk (N = 2,006) and a sample from Lucid that was quota-matched to the national
distribution on age, gender, ethnicity, and geographic region (N = 1,985). The two datasets were
combined for the following analyses (N = 3,991).
Study 2 was conducted during March 2020. We recruited American participants from Amazon’s
Mechanical Turk (N = 1,009). We pre-registered Study 2, with the preregistration available at:
https://osf.io/q6gwy/.
Headlines
Participants in both studies were shown a set of headlines which were randomly selected from a
large pool of actual news headlines, of which 1/3 were true, 1/3 were false, and 1/3 were
misleading headlines (see https://osf.io/q6gwy/ for full list of headlines in each study).
In Study 1, a list of 210 headlines (70 true, 70 false, and 70 misleading) was created from a
variety of online sources (e.g. social media, Snopes, Breitbart). Participants were randomly
assigned to only see one type of news (e.g. true, false, or misleading), and they were shown an
image of each headline as it would appear on Facebook. Each participant rated a random subset
of 10 headlines.
In Study 2, a list of 216 headlines (72 true, 72 false, and 72 misleading) was created from a range
of sources as in Study 1. Some of the headlines were the same as those used in Study 1, but some
were replaced with more recent content. Participants were not restricted to a single type of news
as they were in Study 1, and each participant rated a random subset of 18 headlines.
Content Ratings
Given the work reviewed in the introduction section, which suggests a variety of links between
content dimensions and sharing, we asked participants to rate each headline on the following
dimensions. In Study 1, participants rated Truth (“What is the likelihood that the above headline
is true?”), Importance (“Assuming the headline is entirely accurate, how important would this
news be?”), Familiarity (“Are you familiar with the above headline (have you seen or heard
about it before?)”, Partisanship (“Assuming the above headline is entirely accurate, how
favorable would it be to Democrats versus Republicans?”; we constructed a Politically
Concordant variable by reversing the scores for participants who were Democrats), and
Emotionality (“How exciting is this headline?” and “How worrying is this headline?”). Study 2used the same measures of Truth, Importance, Familiarity, and Partisanship, but measured
Emotionality differently (“How anger provoking is this headline?” and “How anxiety provoking
is this headline”?), and added measures for Humor (“How funny is this headline?”) and
Informativeness (“How informative is this headline?”). Dimension questions were all elicited
using Likert scales, and were asked in fixed order. Last, participants were asked about Sharing
(“If you were to see the above article on social media, how likely would you be to share it?”).
For full materials, see https://osf.io/q6gwy/.
Individual Difference Measures
In both studies, after the news headline tasks, participants completed a variety of individual
difference measures including the Cognitive Reflection Test (Frederick, 2005; CRT) which
measures their tendency to engage in analytic thinking versus simply going with their intuitive
first response, a set of questions to test their political knowledge, a set of questions about social
media usage, a set of political position and preferences questions, as well as demographics
including age, gender, income, and education.
Cognitive Reflection. A 6-item version of the cognitive reflection test (Pennycook & Rand,
2018) was measured. Correct responses were summed, resulting in a score ranging from 0 (less
reflective) to 6 (more reflective).
Political Knowledge. Participants were asked to respond to a 5-item political knowledge quiz.
Each question was shown individually, and participants were given a 10-second window to
respond before the page advanced automatically. Correct responses were summed.
Social Media Frequency. Participants were asked a set of questions regarding social media,
including whether they have a Facebook account (Yes or No) and a Twitter account (Yes or No).
They indicated how frequently they use social media accounts, with options ranging from 1
(never) to 5 (daily).
Political Position & Preferences. Participants were asked a set of questions regarding their
political position and preferences: (1) “Which of the following best described your political
position?” (Democrat, Republican, Independent, or other); (2) “Which of the following best
described your political preference?” (1 = strongly Democratic, 3 = lean Democratic, 4 = lean
Republican, 6 = strongly Republican); (3) they were asked to rate their political position on
social issues and economic issues from 1 (Strongly liberal) to 5 (Strongly conservative); (4) they
were also asked a variety of other questions, including who they voted for during the 2016
presidential election and the 2018 Congressional Election, how they feel towards President
Donald Trump, whether they support or oppose his presidency, and if they plan to vote for him
again in the 2020 election. Since the ratings on these questions were highly correlated with each
other, we used Principal Component Analysis (PCA) to reduce dimensions and construct a more
effective measure. We found the first principal component described most of the variation (about
74%), so this component was used to construct an overall measure of political conservatism.Results
The Latent Structure of the Content Ratings
We begin by assessing how the various content dimensions relate to each other. That is, we ask
to what extent the different dimensions actually reflect a smaller number of underlying latent
dimensions. To do so, we conducted exploratory factor analysis (EFA), used parallel analysis
(Horn, 1965) to determine the number of factors to retain in a principled way, and then used
varimax rotation to determine loadings.
In Study 1, we had 6 content ratings: “true”, “familiar”, “important”, “political concordant”,
“worrying”, and “exciting”. Parallel analysis led us to retain two factors, shown in Table 1:
Factor 1 included high loadings on important (0.784), worrying (0.685), and exciting (0.596);
and Factor 2 included high loadings on familiar (0.577) and true (0.499). For robustness, we also
checked a 3-factor solution (see Table A1 in Appendix), and found that Factor 1 kept the same
structure, while Factor 2 was separated into two factors: one included high loadings on familiar,
and the other included high loadings on true and concordant.
Table 1. Exploratory Factor Analysis
with Varimax Rotation (Study 1, 2-
factor solution) 1. Loadings above 0.4
shown in bold.
Item Factor 1 Factor 2
Important 0.784 0.058
Worrying 0.685 0.081
Exciting 0.596 0.251
Familiar 0.166 0.577
True 0.179 0.499
Concordant -0.008 0.259
In Study 2, we had 8 content ratings: “true”, “familiar”, “important”, “political concordant”,
“anger provoking”, “anxiety provoking”, “funny”, and “informative”, and found a broadly
similar pattern. Parallel analysis again indicated two factors, shown in Table 2. Factor 1 included
high loadings on anger (0.785), anxiety (0.778), important (0.524); and Factor 2 included high
loadings on familiar (0.876), funny (0.617), and true (0.489). Furthermore, Informative loaded
heavily on both factors (0.451 and 0.482). In terms of 3-factor solution (see Table A2 in
Appendix), similar to what we found in Study 1, while Factor 1 kept the same structure, Factor 2
was separated into two factors: one included high loadings on familiar and funny, while the other
included high loadings on true and informative.
1
In Study 1, the proportion of variance explained by Factor 1 and Factor 2 are 25% and 12%, respectively.Table 2. Exploratory Factor Analysis
with Varimax Rotation (Study 2, 2-
factor solution) 2. Loadings above 0.4
shown in bold.
Item Factor 1 Factor 2
Anger 0.785 0.267
Anxiety 0.778 0.361
Important 0.524 0.132
Familiar 0.260 0.876
Funny 0.189 0.617
True 0.240 0.489
Informative 0.451 0.482
Concordant 0.054 0.181
In sum, we find a similar 2-factor structure in both studies1. In particular, Factor 1 captures the
perceived importance and emotional evocativeness of the headlines in both studies, while Factor
2 captured perceived accuracy and familiarity in both studies; with the addition of humorousness
and importance in Study 2 (not collected in Study 1).
Relationships with Sharing Intentions and Headline Veracity
In the previous section, we found the consistent existence of a factor of content dimensions
unrelated to perceived accuracy (Factor 1). Next we ask how social media sharing intentions
relate to the various content dimensions, and in particular to the 2 factors identified above. We
find that all of the content dimensions assessed were positively correlated with sharing intentions
(see Tables 3 and 4). Interestingly, in both studies, all of the correlations were greater than
r=0.30 except for political concordance, which was consistently the least strongly correlated
dimension.
We then explored the relationships between the two factors and sharing intentions. To do so, we
ran OLS regressions with the two factors’ scores and their interaction term as independent
variables, and sharing intentions as the dependent variable (one observation per rating, standard
errors clustered on participant). All variables were standardized. The results are presented in
Table 5. Across both studies, the coefficient on Factor 1 (Study 1: β=.464, SE=.007, pfactors represent distinct routes that lead to increased willingness to share news content – and, most importantly, that Factor 1 (which was unrelated to perceived accuracy but had strong loadings on importance and emotional evocativeness) was a consistent predictor of sharing intentions. This observation suggests that these content dimensions may contribute to the spread of misinformation. Further support for this conclusion comes from examining the relationship with the objective veracity of the news, as opposed to sharing intentions. To do so, we conducted a set of post-hoc analyses where headline veracity was coded as 1 if the news headline was true and 0 if the news headline was false or misleading. We again started with examining the correlations between each content rating and headline veracity (see Tables 3 and 4). Turning to the factors, OLS regressions (with all variables standardized) were run to predict headline veracity with the same set of independent variables (see Table 5). Across both studies, the coefficient on Factor 1 (Study 1: β=-0.071, SE=.010, p
Table 4. The Pearson Correlation between sharing intentions, headline veracity, and content ratings
in Study 2.
1 2 3 4 5 6 7 8 9
1. Sharing
-
Intention
2. Headline
0.05 -
Veracity
3. Anger 0.45 -0.02 -
4. Anxiety 0.54 -0.01 0.72 -
5. Important 0.36 0.05 0.43 0.43 -
6. Familiar 0.65 0.06 0.44 0.52 0.24 -
7. Funny 0.56 -0.09 0.33 0.40 0.08 0.6 -
8. True 0.48 0.28 0.29 0.33 0.26 0.49 0.25 -
9. Informative 0.58 0.13 0.44 0.51 0.47 0.53 0.35 0.52 -
10. Concordant 0.18 0.04 0.08 0.10 0.09 0.15 0.16 0.17 0.15
Table 5. OLS regression analyses (standardized) predicting either sharing intention
or headline veracity using the two factor scores and their interaction term. Standard
errors (clustered on participants) are in parentheses.
Dependent Variable:
Sharing Intention Headline Veracity
Study 1 Study 2 Study 1 Study 2
-0.015* -0.008 0.004 0.007
(Intercept)
(0.009) (0.014) (0.015) (0.008)
0.464*** 0.363*** -0.071*** -0.031***
Factor 1
(0.007) (0.011) (0.010) (0.008)
0.287*** 0.570*** 0.226*** 0.103***
Factor 2
(0.007) (0.011) (0.010) (0.009)
0.095*** 0.054*** -0.028** -0.052***
Factor 1 × Factor 2
(0.006) (0.011) (0.009) (0.009)
Observations 39,635 18,162 39,635 18,162
R2 0.366 0.535 0.048 0.009
Adjusted R2 0.366 0.535 0.048 0.009
Note. *pIndividual Differences in Decision Weights Put on the Two Factors We now investigate individual heterogeneity in the weights placed on these two factors. 3 First, we estimated the coefficients of the two factors for each participant when predicting sharing intentions. 4 In order to better account for both subject-level and headline-level random effects, instead of using OLS regressions as planned in Study 2’s pre-registration, we used Bayesian multilevel models to estimate each individual’s factor coefficients. Next, we ran two OLS regressions (one for each factor) to predict the participant’s coefficient for each of the two factors based on the individual difference measures: demographics (age, gender, education, income), CRT score, political knowledge score, political conservatism, and frequency of social media use. The results are shown in Table 6. For completeness, we also examined the Pearson correlations between the dependent variables (i.e. the coefficients on the two factors when predicting sharing intentions) and the individual difference measures (see Tables 7 and 8), as some of the individual difference measures showed fairly high level of correlation with each other (although multicollinearity was not especially high, with all VIFs .61; see Tables A3 and A4 in Appendix). Two results emerge consistently across both studies. First, in the pairwise correlations, CRT and political knowledge were significantly negatively associated with the coefficients on Factor 1. In the multiple regression including both variables together, only CRT remained significant in Study 2 (although because of the high correlation between CRT and political knowledge in Study 2, r=0.55, the non-significance of political knowledge in the multiple regression should be interpreted with caution). Second, conservatism was significantly positively associated with the coefficient on Factor 1 in both studies. The demographic variables (i.e., age, gender, and income) were significantly associated with either the coefficient on Factor 1 or Factor 2 in Study 1, but these associations did not replicate in Study 2. Social media frequency was not significant associated with the coefficient on either factor in either study. 3 In these participant-level analyses, we excluded participants who gave the same response on the sharing intention question to all headlines (24.9% in Study 1, 22.6% in Study 2), because for these participants there is no variation in the outcome variable to predict. In addition, participants who did not complete the additional measures were also excluded from the following analyses (2.6% in Study 1, 1.4% in Study 2). 4 To make estimation tractable – and given the very small magnitude of the interaction terms in Table 5 – we do not include the interaction term in the individual-level models.
Table 6. OLS regression analyses (standardized) predicting a) coefficient for Factor
1 and b) coefficient for Factor 2 using four additional measures including CRT,
political knowledge, conservatism, social media frequency, and four demographic
measures including age, gender (Female = 1, Male = 0), education (college and more
= 1, others = 0), income (more than $50,000 = 1, others = 0). Standard errors are in
parentheses.
Dependent Variable:
Coefficient for Coefficient for
Factor 1 Factor 2
Study 1 Study 2 Study 1 Study 2
0.000 0.000 0.000 0.000
(Intercept)
(0.018) (0.036) (0.019) (0.035)
-0.040* 0.047 -0.038 0.006
Age
(0.019) (0.037) (0.020) (0.037)
-0.085*** -0.024 -0.001 0.021
Gender
(0.018) (0.036) (0.019) (0.036)
-0.030 -0.053 0.031 0.015
Education
(0.019) (0.038) (0.020) (0.038)
-0.001 -0.041 -0.041* -0.051
Income
(0.019) (0.037) (0.020) (0.037)
-0.184*** -0.123** 0.029 0.080
CRT
(0.020) (0.043) (0.021) (0.043)
Political -0.127*** -0.026 0.018 0.171***
Knowledge (0.020) (0.045) (0.021) (0.045)
0.049** 0.098* 0.028 0.057
Conservatism
(0.019) (0.039) (0.019) (0.039)
Social Media 0.006 -0.008 0.018 0.045
Frequency (0.018) (0.036) (0.019) (0.036)
Observations 2,892 767 2,892 767
R 2
0.083 0.037 0.005 0.050
Adjusted R2 0.081 0.027 0.002 0.040
Note. *pTable 7. The Pearson correlation for the two factor coefficients and the individual difference measures in Study 1 (All variables have been standardized; *p
Table 8. The Pearson correlation for the two factor coefficients and the individual difference measures in Study 2 (All variables have been standardized; *p
Discussion In the current work, we examined how various features of news headlines predict consumers’ social media sharing intentions, and how that varies based on individual characteristics. From two studies with 5,000 participants, we found a consistent 2-factor structure to how different content dimensions related to each other: Factor 1 was highly loaded with “important”, “worrying”, “exciting” in Study 1, and “important”, “anger-provoking”, “anxiety-provoking” in Study 2; Factor 2 was highly loaded with “true” and “familiar” in Study 1, and “true”, “familiar”, and “funny” in Study 2. This factor structure reveals relationships that were not necessarily obvious ex ante, highlighting how importance and emotional evocativeness are distinct from perceived accuracy, and how perceived accuracy and familiarity consistently track each out. These observations help to further illuminate how consumers think about the news they read. Furthermore, each content dimension, as well as both factors, were significantly positively associated with sharing likelihood. Perhaps the most parsimonious interpretation of Factor 1 is that it represents how engaging participants find the headlines to be. This is particularly striking, given that this engagingness factor was positively correlated with sharing intentions, but negatively correlated with headline veracity. False or misleading claims were more engaging – which may help to explain why inaccurate news is often shared as much, or even more, than accurate news (Grinberg et al., 2019; Vosoughi, Roy, & Aral, 2018). This observation coincides with the individual differences associated with weighting this factor more when deciding what to share. Specifically, we found that people with higher CRT scores and more political knowledge – as well as less conservative political inclinations – placed lower average weights on this engagingness factor. These results connect with the prior observation that CRT is associated with more truth discernment in sharing intentions (Pennycook, McPhetres, et al., 2020; Ross, Rand, Pennycook, 2019) as well as actually sharing news from more trustworthy sources on Twitter (Mosleh, et al., 2021). Research also suggests that conservatives share more fake news on Twitter (Grinberg et al. 2019) and Facebook (Guess et al. 2019). Our findings suggest that these differences in sharing behavior may result not from differences in how much people attend to the perceived truth of the headlines (which loaded on Factor 2) but instead from differences in how much they are influenced by the content’s engagingness. This observation resonates with recent findings that the sharing of misinformation is often driven by inattention to accuracy rather than the inability to discern accuracy when attending to it (Pennycook McPhetres, et al., 2020; Pennycook, Epstein et al., 2021). The interpretation of Factor 2 is somewhat less straightforward. Familiarity and perceived truth loaded on this factor in both studies, which might suggest that the factor captures how plausible a headline seems. It is because these results connect with the well-established “illusory truth effect”, whereby repetition (i.e., low novelty) increases perceived accuracy (Hasher, Goldstein, & Toppino, 1977; see Dechêne, et al., 2010 for a review), even for blatantly false fake news (Pennycook Cannon, & Rand, 2018). However, the high loading of “funniness” in Study 2 is somewhat inconsistent with this characterization. Furthermore, the lack of consistent individual difference correlations with the coefficient for Factor 2 also does not help to clarify the factor’s interpretation. Future work should investigate this second factor in more details.
Our findings make several contributions to the existing literature. Our results demonstrate how engagingness is separate from truth, and emphasize the role that engagingness is likely to play in social media consumers’ choice to share inaccurate news online. These results resonate with past research on word of mouth suggesting that content evoking high-arousal emotions is more viral (e.g. Berger & Milkman, 2012), extending those results by demonstrating the disconnect between such emotions and objective truth. This disconnect has important implications for understanding the dynamics of news on social media. In particular, our findings shed light on why false content often spreads online (e.g. Vosoughi, Roy, & Aral, 2018). One might imagine that people share misinformation because consumers purposeful downplay truth. However, our results suggest that it may instead be because consumers are drawn to content that is engaging irrespective of its accuracy – and inaccurate content is often more engaging (likely because content creators can create more engaging content if they are not constrained by the truth). This observation can help to inform attempts of social media companies to reduce the spread of misinformation, as well as news organizations that are seeking to generate news that succeeds online. Furthermore, our work brings together work relevant to online word of mouth from several different disciplines beyond just marketing, a topic that is receiving considerable attention from fields including cognitive psychology (see Pennycook & Rand, 2021 for a review), economics (e.g., Allcott & Gentzkow, 2017), communication and media studies (e.g., Metzger et al., 2021; AI-Rawi, 2019; Kümpel, Karnowski, & Keyling, 2015; Lee & Ma, 2012), and computational social science (e.g., Goel, et al., 2016; Rudat, Buder, & Hesse, 2014). In doing so, we hope to help foster cross- disciplinary work on the important topic of online news sharing. Finally, there are several limitations of the current work that are important to acknowledge, and various important directions for future research. In addition to clarifying the nature of the second factor we identified, future work should investigate the relationship between sharing, veracity, and other content dimensions not considered here. Second, the current work uses self-reported measures of sharing intentions instead of actual sharing decision data from field experiments. Although there is reason to believe that self-report sharing intentions are informative (Mosleh, et al., 2020), future research could design field experiments on the social media platforms such as Twitter to improve ecological validity. Third, since the current work focuses on news sharing decisions, future work could assess how our results generalize to other contexts such as digital marketing related decision tasks, or other online content sharing decisions.
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Advances.Appendix
Table A1. Exploratory Factor Analysis with
Varimax Rotation (Study 1, 3-factor solution).
Loadings above 0.35 shown in bold.
Item Factor 1 Factor 2 Factor 3
Important 0.777 0.013 0.095
Worrying 0.701 0.112 -0.054
Exciting 0.599 0.145 0.173
Familiar 0.136 0.966 0.211
True 0.196 0.221 0.375
Concordant -0.012 0.040 0.404
Table A2. Exploratory Factor Analysis with
Varimax Rotation (Study 2, 3-factor solution).
Loadings above 0.5 shown in bold.
Item Factor 1 Factor 2 Factor 3
Anger 0.787 0.247 0.151
Anxiety 0.770 0.335 0.203
Important 0.490 -0.083 0.423
Familiar 0.259 0.717 0.400
Funny 0.176 0.693 0.142
True 0.157 0.276 0.579
Informative 0.368 0.236 0.685
Concordant 0.023 0.141 0.169Table A3. The VIF and Tolerance of the individual difference measures (Study 1). IVs VIF Tolerance Age 1.118 0.894 Gender 1.061 0.943 Education 1.140 0.877 Income 1.127 0.887 CRT 1.233 0.810 Political Knowledge 1.300 0.769 Conservatism 1.079 0.926 Social Media Frequency 1.021 0.979 Table A4. The VIF and Tolerance of the individual difference measures (Study 2). IVs VIF Tolerance Age 1.093 0.915 Gender 1.031 0.970 Education 1.158 0.863 Income 1.081 0.925 CRT 1.489 0.671 Political Knowledge 1.617 0.619 Conservatism 1.199 0.834 Social Media Frequency 1.036 0.966
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