Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

Leveraging Social Media for Human Factors Research in Health Care

2021 ◽  
Vol 65 (1) ◽  
pp. 82-85
Author(s):  
Rupa S. Valdez ◽  
Annie T. Chen ◽  
Andrew J. Hampton ◽  
Kapil Chalil Madathil ◽  
Elizabeth Lerner Papautsky ◽  
...  
Keyword(s):  
Health Care ◽  
Social Media ◽  
Human Factors ◽  
Disaster Response ◽  
Academic Research ◽  
Gender Based Violence ◽  
Ethical Implications ◽  
Social Media Platforms ◽  
Study Participants ◽  
Human Factors Research

There has been a significant increase in using social media for academic research and there is an opportunity for human factors professionals to incorporate these platforms into their research. Social media platforms provide a rich space to study extant data on health information communication, behaviors, and impacts and to recruit study participants. In this session, panelists will discuss using social media to study health-related topics including health management, gender-based violence, disaster response, self-harm, patient ergonomics, and secondary impacts of the COVID-19 pandemic. They will share how they have collected and analyzed data and recruited study participants from social media platforms such as Twitter, Reddit, and Facebook. They will also speak to the benefits and challenges of as well as ethical implications for using social media for research. There will be space for a moderated discussion to identify ways social media can be leveraged for human factors research in health care.

scholarly journals Tweeting after an earthquake: user localization and communication patterns during the 2012 Emilia seismic sequence

2016 ◽  
Vol 59 (5) ◽  
Author(s):  
Francesca Comunello ◽  
Lorenza Parisi ◽  
Valentino Lauciani ◽  
Federica Magnoni ◽  
Emanuele Casarotti
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Disaster Response ◽  
Communication Patterns ◽  
Seismic Sequence

The main goal of this paper is analysing how user’s location, relative to the epicenter of an earthquake, affects the different tweeting strategies adopted. For this purpose, we analyze a dataset of tweets that were generated around the 2012 Emilia earthquakes and that are geolocalized in Italy. In our analysis, we rely on existing literature on social media and natural disasters, considering literature exploring interactions and influence on Twitter, and literature focusing on the role of geolocalized user-generated information in disaster response.

Social Media and Citizen Engagement

2013 ◽  
pp. 225-238
Author(s):  
Charlie E. Cabotaje ◽  
Erwin A. Alampay
Keyword(s):  
Social Media ◽  
Disaster Response ◽  
Real Life ◽  
The Philippines ◽  
The Internet ◽  
Citizen Engagement ◽  
Face To Face ◽  
First Case ◽  
Use Of Social Media

Increased access and the convenience of participation to and through the internet encourage connectivity among citizens. These new and enhanced connections are no longer dependent on real-life, face-to-face interactions, and are less restricted by the boundaries of time and space (Frissen, 2005). In this chapter, two cases from the Philippines are documented and assessed in order to look at online citizen engagement. The first case looks at how people participate in promoting tourism in the Philippines through social media. The second case involves their use of social media for disaster response. Previous studies on ICTs and participation in the Philippines have looked at the role of intermediaries (see Alampay, 2002). Since then, the role of social media, in particular that of Facebook and Twitter, has grown dramatically and at times completely circumvents traditional notions of intermediation. The role of Facebook, in particular, will be highlighted in this chapter, and the authors will analyze its effectiveness, vis-à-vis traditional government channels for communication and delivery of similar services. By looking at these two cases and assessing the abovementioned aspects, it is hoped that the use of social media can be seen as an integral part of e-governance especially in engaging citizens to participate in local and national governance.

scholarly journals Rapid Multi-Dimensional Impact Assessment of Floods

2020 ◽  
Vol 12 (10) ◽  
pp. 4246 ◽  
Author(s):  
David Pastor-Escuredo ◽  
Yolanda Torres ◽  
María Martínez-Torres ◽  
Pedro J. Zufiria
Keyword(s):  
Social Media ◽  
Impact Assessment ◽  
Rural Areas ◽  
Disaster Response ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Sensor Data ◽  
Mobile Phone Data ◽  
Aggregated Data ◽  
The Impact

Natural disasters affect hundreds of millions of people worldwide every year. The impact assessment of a disaster is key to improve the response and mitigate how a natural hazard turns into a social disaster. An actionable quantification of impact must be integratively multi-dimensional. We propose a rapid impact assessment framework that comprises detailed geographical and temporal landmarks as well as the potential socio-economic magnitude of the disaster based on heterogeneous data sources: Environment sensor data, social media, remote sensing, digital topography, and mobile phone data. As dynamics of floods greatly vary depending on their causes, the framework may support different phases of decision-making during the disaster management cycle. To evaluate its usability and scope, we explored four flooding cases with variable conditions. The results show that social media proxies provide a robust identification with daily granularity even when rainfall detectors fail. The detection also provides information of the magnitude of the flood, which is potentially useful for planning. Network analysis was applied to the social media to extract patterns of social effects after the flood. This analysis showed significant variability in the obtained proxies, which encourages the scaling of schemes to comparatively characterize patterns across many floods with different contexts and cultural factors. This framework is presented as a module of a larger data-driven system designed to be the basis for responsive and more resilient systems in urban and rural areas. The impact-driven approach presented may facilitate public–private collaboration and data sharing by providing real-time evidence with aggregated data to support the requests of private data with higher granularity, which is the current most important limitation in implementing fully data-driven systems for disaster response from both local and international actors.

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