“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

Abstract

In this paper, we analyze more than 16 million tweets that have been sent from 6.1 million Twitter accounts and are related to nine natural disasters. As part of our analysis, we identify eight basic emotions conveyed in these tweets. We found that during natural disasters, social media messages at first predominantly express fear, while sadness and positive emotions increase in the immediate aftermath of the event. In this context, positive emotions contribute to the social phenomenon of emotional bonding and are often related to compassion, gratitude, as well as donations for disaster relief. In our analysis, we found that the users’ emotional expressions directly contribute to the emergence of the underlying communication network. In particular, we identified statistically significant structural patterns that we call emotion-exchange motifs and show that: (1) the motifs 021U and 021D are common for the communication of all eight emotions considered in this study, (2) motifs which include bidirectional edges (i.e. online conversations) are generally not characteristic for the communication of surprise, sadness, and disgust, (3) the structural analysis of a set of emotions (rather than a single emotion) leads to the formation of more complex motifs representing more complex social interactions, and (4) the messaging patterns emerging from the communication of joy and sadness show the highest structural similarity, even reaching a perfect similarity score at some point during the data-extraction period.

Hostility on Twitter in the Aftermath of Terror Attacks

“…In the context of the Paris terrorist attacks in November 2015, Garcia and Rimé, [46] show an immediate surge in negative affect in Tweets, including anxiety, sadness, and anger, after the attack, which leveled off within several days. However, the rise in increased solidarity after the attack was long-term [46] -a pattern consistent with the response to other types of disasters [47]. Attitudinal survey studies have also shown that the potential effects of terrorism on hostility tend to be short-lived [10] and abate over time [11].…”

How Does the World View China’s Carbon Policy? A Sentiment Analysis on Twitter Data

“…In future research, we will further consider the mechanisms related to the impact of social media topics related to China's carbon policy on China's image and the impact of social media on China's carbon policy-making. We will further investigate and analyze the nature of Twitter accounts and Twitter user categories, in addition to screening robot accounts and determining whether the referenced Twitter account data are sourced from media or personal accounts, to further explore the impact of different types of Twitter accounts on China's carbon policy [81][82][83][84]. Due to the limited use of Twitter in China, the degree to which people use Twitter in different regions will affect the research results.…”

On the dynamics of political discussions on Instagram: A network perspective

“…In their most basic form, these structures are represented by different motifs (e.g., triangular motifs, star, structural hubs, etc) [46,47]. Some studies focused on characterizing many of these networks [48,42], while others analyzed the relationship between motifs and some specific phenomena of interest on a particular network [49,50]. Focusing on revealing communities emerging from co-interactions, which is our present goal, some approaches are concentrated at uncovering communities that are based on particular structural patterns, specifically, on motifs [51,52,53,54].…”