Using social media for sub-event detection during disasters

AbstractSocial media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach

The role of bots in spreading conspiracies: Case study of discourse about earthquakes on Twitter

In this paper, we identified seven most widely spread conspiracy discourses about earthquakes. These conspiracy discourses link earthquakes to military activities like secret nuclear bomb testing, God's Providence like the punishment of humans for their sins, space activities like aliens visiting our planet, the US secret weather control program HAARP, tests of the Large Hadron Collider, fracking projects, and freemasonic plots. Following the major earthquake in Indonesia at the end of November 2022, we extracted data from Twitter by keywords using the Hoaxy tool for tracking the spread of information on Twitter. Applying the Bot Sentinel tool, we also got data on the sentiment of the users. The divine and military discourses dominated the conspiracy discussion, followed by the discussions about extraction and HAARP. Though there were more human-like accounts than bot-like accounts, we found a positive correlation between the frequency of tweets on the conspiracy discourses and the bot scores of the accounts, which suggests that bot-like accounts were tweeting more than human-like accounts. It was also found that normal accounts tweeted more than toxic accounts, and there was a positive relationship between the bot score and the toxicity level of an account. It suggests that bot-like accounts were involved more in disruptive activities than human-like accounts

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

The Impact of Social Media Knowledge Acquisition on Innovation and Financial Performance of the Firm: A Mixed Methods Approach

Firms have been increasingly applying different social media initiatives for different organizational objectives, but which initiatives have an impact on which objectives is still not well understood. In this research, we empirically examine the impact of two social media initiatives - social media information collection and social media proactive market orientation- on innovation and financial performance at the firm level. We also examine the potential role of three mediators within these relationships: IT infrastructure, social capital, and organizational capital. To do so, this study follows a mixed methods approach, where both quantitative and qualitative data were collected and analysed. The purpose of the quantitative data is to test the hypotheses and the developed conceptual model, while the purpose of the qualitative data is to triangulate the quantitative results. Data were collected through two surveys, one quantitative and one qualitative, from firms in the United States. Structural Equation Modelling was used to analyse the quantitative data, while thematic analysis was used to analyse the qualitative data. Our findings provide empirical evidence on the positive effects of both social media initiatives on innovation and financial performance, and on the roles of IT infrastructure, social capital, and organizational capital within these relationships