For the different subsamples I estimate different models. Due to the count nature of the data from online social media, I estimate a model using a Poisson regression with day specific fixed effects, clustering the standard errors by the publishing date. The estimation equation for sharing articles on Facebook, Twitter and Google+ looks as follows:
ππππ(π_πΏππππ = π_πΏππππ ππ‘|πππ‘) =πβπππ‘πππ‘π_πΏππππ ππ‘
π_πΏππππ ππ‘! with Ξ»it = ππππβ²π·+ππ‘,
where π_πΏππππ ππ‘ measures the number of recommendations for article π at day π‘ and πππ‘ is the vector describing article π on the different dimensions.
For the most emailed list I estimate a logistic regression β which is closest to the model presented by Berger and Milkman (2012) β with day specific fixed effects. The estimation equation looks as follows:
ππππ(πππ π‘πππππππππ‘ = 1|πππ‘) = 1 1 + πβ(πππβ²π·+π)
For the small random subsample of hand-coded articles I do not estimate fixed effects models due to sparsely distributed data over the days. Instead, I estimate a logistic regression for the most emailed list and for three social media I estimate negative binomial regressions20 including month and weekday dummies to control for the release date and using robust standard errors to account for heteroscedasticity.
6.5 Results
The models 1-4 (Table 36) present the results for the sample of manually coded articles, models 5-8 for the full sample. The Wald tests in all models show that at least one of the independent variables is not equal to zero (all p <.001 with the exception of Model 1).
20 In contrast to the full sample where I use Poisson regression with fixed effects, I use negative binomial regression, because the latter captures better the distribution of the data in the small sample.
Overall, some findings across communication media are consistent but there are also some differences with respect to the study by Berger and Milkman (2012). I can confirm the positive relation of positivity and emotionality to contentβs virality, although this effect is not persistently significant for all communication media.
Models 5-8 show that the three dimensions anger, anxiety and surprise have a positive effect on sharing probability, a result which seems to be robust for social media as communication media in general. Anger evoking content goes viral in all three social media supporting findings by Berger and Milkman (2012) (p < .001). Similarly, surprise is positively related to contentβs virality (p < .001 for Facebook and Google+, p < .05 for Twitter) and anxiety inducing content is less likely to be shared by users with their peers via social media (p < .001).
Figure 14. Scatter plots depicting the relation between positivity and the number of Likes, Tweets, One Ups and Most Emailed (0/1).
With respect to other content dimensions we observe subtle differences for the communication media: The effects for Facebook differ substantially from the results presented by Berger and Milkman (2012). While content that evokes awe and is of high practical utility goes viral on Twitter and Google+ (which is in line with the results presented by Berger and Milkman (2012)), the opposite is true for Facebook. The finding that useful content is less shared on Facebook supports the findings by Schulze et al. (2014), who reveal that utilitarian apps are less suited to be broadcasted on Facebook. Further, I find that sadness evoking content can go viral on Facebook which is also contradictory to the results presented by Berger and Milkman (2012) but in line with anecdotal evidences. For example, a very sad article about a drowned dog went viral, being with 62,229 Likes on Facebook one of the most shared articles in Germany in 2013. Likewise,
0.2.4.6.8 1
Mostemailed
-10 -5 0 5 10 15
Positivity
0
10000200003000040000
#Likes
-10 -5 0 5 10 15
Positivity
0
5001000
#Tweets
-10 -5 0 5 10 15
Positivity
0
100200300400
#Oneups
-10 -5 0 5 10 15
Positivity
interesting articles evoking awe have a lower probability to be shared on Facebook. These results could be partially explained by the high usage and user heterogeneity on Facebook.
On top of the findings, I find an inverted U-shape relationship between positivity and the number of recommendations in the respective communication media (as depicted in Figure 14). The models 9-12 in Table 36 show estimation results when a quadratic term for positivity is included21. With the exception of the most emailed list, the quadratic term of positivity is negative and highly significant for all three social media (p < .001). The effects of the other dimensions remain unchanged and for the number of Tweets the first term of positivity turns to be significant. The likelihood ratio test shows that including the quadratic term substantially increase the explanation power of the model (p < .001). This finding implies that content has an optimal level of positivity and that we cannot support the suggestion to craft content as positive as possible. Apparently, the users value objectively written articles which have a balanced amount of positive and negative words. Interestingly is this effect for the most emailed list model not significant. On the one hand, it supports the findings by Berger and Milkman (2012) and on the other hand it implies that there are differences between e-mail and social media as communication media or focusing on a binary dependent variable as in Berger and Milkman (2012) or for the most emailed list model leads to a loss of information. Future research might concentrate on these dissenting results.
6.6 General Discussion
In this replication study of Berger and Milkman (2012) for German articles in the context of four different communication media, I can largely confirm their results but the findings are also partially different. I find in the sample a positive relationship between emotionality and an inverted U-shaped effect between positivity and virality in social media. This can be caused by cultural differences but might also be a sign that sentiment in press articles are not highly valued by the audience as objectivity, neutrality and fact verification are more important than the frequent use of positive and negative words. I find strong evidence for the positive effect on virality of the emotions anger, anxiety and surprise in all three social media. Further, Facebook users exhibit partially another sharing behavior than users on Twitter and Google+.
These ambiguous results suggest refraining from simple generalization: The drivers and moderators for content diffusion might depend on the context, the communication medium, and senderβs traits and might also be different for different cultures. This study provides then directions for future research on diffusion processes in different communication media.
21 I also tested whether the quadratic term of emotionality has an effect on contentβs virality, but the results were insignificant.
# Likes # Tweets # Plus-ones Most emailed Positivity Emotionality Anger Anxiety Awe Sadness Surprise Utility Interest Coded # Words Time of day Complexity Top featured In first position In teaser Hours on homepage Hours in top Hours first position Hours in teaser
# Tweets 0.55* 1
# Plus-ones 0.61* 0.71* 1 Most emailed 0.27* 0.27* 0.35* 1
Positivity 0.01 0.01 0.02 0.03* 1.00
Emotionality 0.02* 0 0 0.01 -0.08 1
Anger 0 -0.01 -0.01 0 -0.33* 0.06 1
Anxiety 0 -0.01 -0.01 0 -0.33* 0.06 0.43* 1 Awe -0.01 -0.01* -0.01 0 0.20* 0.04 -0.27* -0.11* 1 Sadness 0 -0.01 -0.01 0 -0.35* 0.11* 0.35* 0.49* -0.10 1
Surprise 0 -0.01 0 0 0.03 -0.09 0.03 0.11 0.21* 0.05 1
Utility -0.01 -0.01 0 0 0.23* -0.00 -0.09 -0.10 0.08 -0.13 -0.02* 1 Interest 0 -0.01 0 0 -0.05 0.03 0.30 * 0.35* -0.01 0.14* 0.23* 0.01 1 Coded -0.01 -0.02* -0.01 0 0 -0.01 0.89* 0.89* 0.9* 0.88* 0.93* 0.87* 0.96* 1
# Words 0.06* 0.15* 0.12* 0.11* 0.1* 0.08* -0.03* -0.03* -0.03* -0.03* -0.04* -0.03* -0.03* -0.04* 1 Time of day 0.02* 0 0.05* 0.06* 0.04* -0.01 0.01 0.02* 0.02* 0.01 0.01 0.01* 0.02* 0.01* 0.09* 1 Complexity 0.03* -0.08* -0.02 0.03* 0.24* 0.17* -0.02* -0.02* 0.01 -0.01 0 0 -0.01 0 0.15* 0.03* 1 Released as top featured 0.1* 0.26* 0.12* 0.09* -0.02* 0.05* -0.01 -0.01 -0.01* -0.01 -0.01 -0.01 -0.01 -0.01 0.15* -0.06* 0.04* 1 Released on first position 0.11* 0.22* 0.14* 0.13* 0.07* 0.03* -0.01 -0.01 0 -0.01 0 0 0 -0.01 0.19* -0.03* 0.09* 0.45* 1 Released as teaser 0.06* 0.17* 0.07* 0.02* -0.04* 0.01 0 0 0 0 0 -0.01 0 0 0.08* -0.04* -0.05* -0.11* 0.12* 1 Hours on landing page 0.04* 0.02* 0.08* 0.04* 0.04* 0 0 0 0 0 0.01 0.01 0 0 0.08* 0.05* 0.05* 0.05* 0.14* -0.01* 1 Hours as top featured 0.03* 0.03* 0.05* 0.01 0 0.01* 0 0 0 0 0 0 0 0 0.04* 0.01 0.02* 0.11* 0.06* -0.01 0.62* 1 Hours as first position 0.08* 0.11* 0.09* 0.13 0.06* 0 -0.01 0 0 0 0.01 0.01 0 0 0.17* -0.06* 0.1* 0.2* 0.44* 0.03* 0.27* 0.11* 1 Hours as teaser 0.05* 0.12* 0.04* 0.02* -0.02* 0 0 0.01 -0.01 0 0 0 0 0 0.04* -0.05* -0.02* -0.06* 0.07* 0.52* 0 -0.01 0.04* 1 Note: * p < 0.05
Table 35. Correlation matrix
Most emailed # Likes # Tweets # Plus-ones Most emailed # Likes # Tweets # Plus-ones Most emailed # Likes # Tweets # Plus-ones