Modelling and Analyzing the Semantic Evolution of Social Media User Behaviors during Disaster Events: A Case Study of COVID-19

Abstract

Public behavior in cyberspace is extremely sensitive to emergency disaster events. Using appropriate methodologies to capture the semantic evolution of social media users’ behaviors and discover how it varies across geographic space and time still presents a significant challenge. This study proposes a novel framework based on complex network, topic model, and GIS to describe the topic change of social media users’ behaviors during disaster events. The framework employs topic modeling to extract topics from social media texts, builds a user semantic evolution model based on a complex network to describe topic dynamics, and analyzes the spatio-temporal characteristics of public semantics evolution. The proposed framework has demonstrated its effectiveness in analyzing the semantic spatial–temporal evolution of Chinese Weibo user behavior during COVID-19. The semantic change in response to COVID-19 was characterized by obvious expansion, frequent change, and gradual stabilization over time. In this case, there were obvious geographical differences in users’ semantic changes, which were mainly concentrated in the capital and economically developed areas. The semantics of users finally focused on specific topics related to positivity, epidemic prevention, and factual comments. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions. In emergency situations, this model could improve situational assessment, assist decision makers to better comprehend public opinion, and support analysts in allocating resources of disaster relief appropriately.

Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial–temporal–semantic features

“…We used the topic extraction and classification model proposed in our previous studies (Han & Wang, 2019, 2022) to process Weibo texts. The model combines the latent Dirichlet allocation (LDA) model and the random forest (RF) algorithm.…”

“…We assigned each Weibo text to a topic that most closely resembled the probabilities in the document‐topic lists. Based on the topic‐terminology lists, 20 topics were generalized into 15 by merging similar topics and discarding irrelevant ones (Han & Wang, 2022). The sample Weibo texts labeled with topics were then input into the RF algorithm as training samples.…”

“…Based on the topicterminology lists, 20 topics were generalized into 15 by merging similar topics and discarding irrelevant ones F I G U R E 1 Process framework for mining of TGS-based user behavioral pattern. (Han & Wang, 2022). The sample Weibo texts labeled with topics were then input into the RF algorithm as training samples.…”

Exhaustive Insights Towards Social-Media Driven Disaster Management Approaches

“… This type of method mainly emphasizes channeling the identified events of disaster to an appropriate communication channel to forward the information to emergency services. Some of the existing research frameworks towards information dissemination implemented are as follows Mobile computing-based emergency management (Astarita et al [37]), Geospatial-information based disaster management (Ghawana et al [38]), User behavior centric framework for disaster screening (Han & Wang [39]), Hubframework connecting the critical community with local emergency management team (Mitcham et al [40]), Micro-Macro level-based disaster alters using social network (Samaddar et al [41]), Edge computing-based named data network for disaster response system (Tran & Kim [42]), Acquisition framework for disaster location analysis (Yang et al [43]), Multimodal framework for disaster data evaluation (Zhang et al [44]), Model for Dynamic theme propagation for rainstorm detection (Zhang et al [45]), Integrated machine learning and spatiotemporal analytical framework (Zhang et al [46]). A closer look into these study models shows that they are mainly meant for the authorities to use social media to propagate safety guidelines, evacuation routes, and emergency information to the affected population.…”

Revealing the Perceived Community Resilience During the Pandemic in the City Area of Harbin through Social Media

“…Some existing studies indicate that positive emotions can co-occur with negative emotions during a crisis, and these positive emotions can play a significant role in mitigating the adverse effects of the crisis [43]. While previous research has examined emotional responses to crises in various contexts, such as flooding, hurricanes, and influenza, and in relation to constructs like trust, information processing, and perceived responsibility, there has been relatively less investigation into the connection between emotions and perceived community resilience [44].…”