Online social networks (OSNs) play a significant role during crisis events by offering a convenient channel for information seeking, social bonding, and opinion sharing. In this context, people express their fear, panic, shock, as well as gratitude, well-wishing, and empathy as a crisis event evolves over time. Though emotional responses during crisis events have been studied both in offline and online settings, it is yet unclear which communication structures are representative for the exchange of specific types of emotions. In this paper, we report on new findings which indicate that not all negative emotions are exchanged in the same way. In particular, we used emotion-exchange motifs to compare the structure of emotion-annotated communication networks that resulted from 18 crisis events. Our findings clearly indicate that 1) exchanges of sadness on the one hand, and joy/love on the other show more structural similarity than any other pair of emotions, 2) emotion-exchange networks can be clustered into two families, each of which includes different types of emotions, 3) membership in the two families of emotion-exchange networks fluctuates over time. A related data-set is available for download from IEEE DataPort, DOI: 10.21227/yajb-6y77.

In this paper, we analyze the message exchange patterns that emerge when social bots and human users communicate via Twitter. In particular, we use a multilayer network to analyze the emergence of the corresponding representative and statistically significant sub-graphs (so called motifs). Our analysis is based on two recent riot events, namely the Philadelphia Super-bowl 2018 riots and the 2017 G20 riots in Hamburg (Germany). We found that in these two events message exchanges between humans form characteristic and re-occurring communication patterns. In contrast, message exchanges including bots occur rather sporadically and do not follow a particular statistically significant pattern.

In this paper, we present a study on 4.4 million Twitter messages related to 24 systematically chosen real-world events. For each of the 4.4 million tweets, we first extracted sentiment scores based on the eight basic emotions according to Plutchik's wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the spread of information. We found that in general OSN users tend to conform to the emotional valence of the respective real-world event. However, we also found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted emotions in the corresponding tweets (i.e. positive messages). To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis postulates that positive emotions serve as an antidote during negative events.