Effect of emotional contagion

We here want to test the hypothesis that emotional contagion occurs among social media users, as suggested by recent works on various social platforms [21, 29]. The idea is that emotions can be passed via online interactions even in absence of non-verbal cues typical of in-person interactions, which are deemed by traditional psychology to be an essential ingredient for emotional contagion [20]. To test this hypothesis, we need to reconstruct the emotions conveyed by the tweets each user was exposed to before posting their own tweets: this will allow us to determine whether the stimuli are correlated with the responses, namely the emotions subsequently expressed by the user.

Our study is purely observational, as we don’t perform any type of controlled experiment differently from other works [29]. We aim to show that the average sentiment of tweets preceding a positive, negative or neutral tweet are significantly different, and determine the effect size which, even if small, at scale would have important implications.

To do so, we adopt the following reshuffling strategy aimed at determining the baseline distributions of positive, neutral, and negative contents independently of emotional contagion: for each user u in the set of 3,800 users, and for each tweet t u produced by u, we have the history ℓ(t u ) of all tweets preceding t u in the 1 hour period prior to t u ’s publication, and we record how many such tweets s ℓ(t u ) = |ℓ(t u )| user u was exposed to. We then put all these tweets ℓ(t u ), that represent the stimuli prior to the users’ activities, for all tweets, for all users, in one single bucket.

To create our reshuffled null model that discounts for the effect of emotional contagion, we therefore sample with replacement from bucket B, for each tweet t u of each user u, a number of tweets equal to the size s ℓ(t u ) . The results for sampling without replacement are substantially identical. At the end of the procedure, we obtain a baseline distribution of positive, neutral, and negative sentiment prior to the publication of any tweet, which discounts for the effect of exposure and the possibility of emotional contagion. The baseline distribution of sentiment in the null model is displayed in Fig 1: the proportion of positive, neutral, and negative sentiment after the exposure reshuffling is equal to, respectively, 34.44% (±0.07), 48.27% (±0.06), and 17,29% (±0.08). These proportions reflect the three classes of emotions defined as follows: negative (S ≤ −1), neutral (S = 0), and positive (S ≥ 1).

To verify the hypothesis of emotional contagion, we divide all tweets t u posted by each user u, in three categories (positive, neutral, and negative) according to their sentiment. For each category, then, we generate the distribution of fraction of positive, neutral, and negative sentiments observed in the stimuli, the tweets produced by u’s followees prior to the posting of each t u . The results, displayed in Fig 1, are interpreted as follows: the three stacked-columns identify the distributions of sentiment prior to posting (from left to right) a negative, neutral, or positive tweet. For example, a user in our set prior to posting a negative tweet is exposed, on average, to 21.63% (±0.17) negative tweets, 45.02% (±0.11) neutral, and 33.35% (±0.13) positive ones. This signifies an over-exposure to 4.34% more negative tweets, at the expenses of 1.09% less positive ones, if compared with our null model of Fig 1. Similarly, prior to posting a positive tweet, a user in our dataset is exposed, on average, to 16.00% (±0.12) negative tweets, 45.05% (±0.11) neutral, and 38.94% (±0.14) positive ones. This amounts for an over-exposure of 4.50% more positive tweets, at the expenses of 1.29% less negative ones, if compared with the null model. Notably, the distribution of the sentiment of tweets before the posting of a neutral one matches almost perfectly the distribution of the null model in Fig 1, suggesting that no emotional contagion occurs in the case of neutral tweets. To prove the statistical significance of these differences, we run a Mann–Whitney U test between the observed distributions in presence of emotional contagion, and the expected baseline of the null model. Both p values for negative and positive emotional contagion tests are p < 10−6 while no significant difference occurs for the neutral case; the strength of the statistical significance is further illustrated by the narrow error bars in Fig 1. The distributions of the positive and negative stimuli, respectively, before positive and negative responses, are also reported in Fig 2.

PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Fig 2. Distributions of positive and negative stimuli before positive and negative responses. The four quadrants show the probability distributions of a negative response prior to a negative (bottom left) or positive (bottom right) stimulus, or a positive response prior to a negative (top left) or positive (top right) stimulus. https://doi.org/10.1371/journal.pone.0142390.g002

These results suggest the presence of emotional contagion for both negative and positive sentiment, and seem to show that no emotional contagion occurs prior to posting neutral contents. To verify that these findings were not strongly dependent on some particular conditions, we performed additional experiments and observed consistent results across different comparable datasets (not discussed here to avoid confusion) and sampling methods.

To further validate this hypothesis, and in particular to focus only on positive and negative contagion, we here propose another measure, that we call valence, that can be computed on any set (bucket) of tweets for which the sentiment is computed. Given a bucket of tweets b, its valence V(b) is given by the following formula: (2) where p b and n b represent, respectively, the fraction of positive and negative tweets in bucket b. This measure ranges between -1 and +1: the lower the score, the larger the disproportion toward negative emotion, and vice-versa.

Since for each tweet t u produced by each user u we already obtained the history ℓ(t u ) of all tweets preceding t u in the 1 hour period prior to t u ’s publication, we can compute the valence scores V(ℓ(t u )) for all histories. This allows us to represent the difference in intensity between positive and negative stimuli each user u was exposed to prior to posting each tweet t u . Therefore, we calculate the valence scores V(ℓ(t u )) for all tweets t u in our dataset. This generates a distribution of values between -1 and +1, each value representing the valence of the stimulus of the associated tweet. We then bin these stimuli valence values, in 20 bins of length 0.05 (see the x-axis of Fig 3). Each bin x b contains, again, a set of tweets (the responses) for which we already calculated the sentiment (positive, negative, or neutral). We can calculate also the valence of each x b . Such values will represent the response valence for a given value (bin) of stimulus valence. The results, illustrated in Fig 3, show a very strong linear relationship (R2 = 0.975) between the valence of the stimulus and the valence of the response. For example, a very strong negative stimulus with valence -1 generates a response valence of about -0.8. Similarly, a very strong positive stimulus of valence +1 will trigger a response of valence around +0.6. Other regression models have also been tried, on this and other similar datasets: the linear model seems to best capture the stimulus-response dynamics without over-fitting the data. These results suggest a common mechanism of contagion in both negative and positive contents: in general, a strongly negative stimulus is followed by negative responses, while a strongly positive stimulus generates positive responses. Neutral stimuli also trigger neutral responses.