Understanding the Behavior of Filipino Twitter Users during Disaster

Lee, John Boaz; Ybañez, Michael; Leon, Marlene M. De; Estuar, Regina E.

doi:10.5176/2251-3043_3.2.254

Cited by 5 publications

(5 citation statements)

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“…They also identified broad trends on the usage of words by the participants and provided a ranking of used words. Similar work has also been done to find trending topics, most favorable language used during disaster and emotions present during disaster occurrence in tweets of Filipino users [10]. This study used Latent Dirichlet Allocation and Principal Component Analysis to extract the various topics discussed in the event of a disaster and predicted the most likely topics which affected people may talk about.…”

Section: Related Workmentioning

confidence: 97%

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Disaster analysis through tweets

Shekhar

Setty

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Social networks offer a wealth of information for capturing additional information on people's behavior, trends, opinions and emotions during any human-affecting events such as natural disasters. During disaster, social media provides a plethora of information which includes information about the nature of disaster, affected people's emotions and relief efforts. In this paper we propose a natural-disaster analysis interface that solely makes use of tweets generated by the Twitter users during the event of a natural disasters. We collect streaming tweets relating to disasters and build a sentiment classifier in order to categorize the users' emotions during disasters based on their various levels of distress. Various analysis techniques are applied on the collected tweets and the results are presented in the form of detailed graphical analysis which demonstrates users' emotions during a disaster, frequency distribution of various disasters and geographical distribution of disasters. We observe that our analysis of data from social media provides a viable, economical, uncensored and real-time alternative to traditional methods for disaster analysis and the perception of affected population towards a natural disaster.

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Section: Related Workmentioning

confidence: 97%

“…Inspired by the authors work in [8], [9] and [10] we propose an automated process of analyzing users' emotions and geographic distribution of disasters using tweets from twitter.…”

Section: Related Workmentioning

confidence: 99%

Disaster analysis through tweets

Shekhar

Setty

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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“…LDA has been used in several studies to examine disaster-related social media content. For example, Lee et al (2013) In the present study, LDA topic modeling is used to identify topics of the tweets in four disaster stages of Hurricane Irma.…”

Section: Topic Modelingmentioning

confidence: 99%

Understanding public opinion in different disaster stages: a case study of Hurricane Irma

Lachlan

Ellis

et al. 2019

INTR

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Purpose Social media, such as Twitter, has become the first and the most frequent place to visit in order to gain information and establish situational awareness in emergencies and disasters. The purpose of this paper is to examine public opinion on Twitter in different disaster stages using the case of Hurricane Irma. Design/methodology/approach More than 3.5m tweets capturing the entire disaster lifecycle were collected and analyzed. Topic modeling was used to generate topics at each disaster stage based on Fink’s (1986) four-stage model of crisis and disaster: prodromal, acute, chronic and termination stages. Findings The results revealed that media reliance varied across different stages. All topics in the prodromal stage were associated with the early warning and real-time news. The topic of lessons learned from Hurricane Harvey was the most popular at this stage. The acute stage recorded the highest number of daily tweets. The most popular topic was the safety of people and animals. In the chronic stage too, the safety of people and animals remained a major concern. Heroic and anti-social behaviors also received substantial attention. In the termination stage, climate change was the most frequently discussed topic. Politics-related discussions were heated. Originality/value The results extended and enhanced the four-stage model of crisis and disaster. These findings can help government agencies and crisis managers address audience needs effectively at various crisis stages in a timely manner.

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“…Like natural language, hashtags are oen used inconsistently [26]. is tendency toward broad but inconsistent use can be problematic in the context of disasters where people tend to post a wide variety of message types [1,4,14,29,42]. While many messages are informative [22], meaning they provide situational information about the disaster, including updates regarding aected individuals, infrastructure, donations and volunteer activities, cautions, and other useful emergency information, many others are non-informative [22], such as those that provide emotional support or express general disaster related opinions [16,29,43].…”

Section: Background and Related Workmentioning

confidence: 99%

The Signals and Noise

Margolin

et al. 2017

Proceedings of the 2017 ACM on Web Science Conference

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Web-based social and communication technologies enable citizens to self-organize relief eorts in response to crises. is work focuses on a question fundamental to the concept of collective intelligence: how eective are such self-organized channels, ungoverned by any central authority, in conforming to their intended function? In this study we examine the hashtag #PorteOuverte ("#OpenDoor") introduced during the 2015 Paris terrorist aacks, as an "improvised logistical channel" (ILC) to help individuals to nd a safe shelter near the aack sites. We analyze the dynamics and eectiveness of #PorteOuverte by comparing its proportion of relevant logistical messages-individuals requesting or oering shelterto other messages such as those oering emotional consolation or commenting on the hashtag itself. Our results reveal that the vast majority of messages are not relevant, however the crowd senses and spreads relevant messages more than others. We further demonstrate that relevant messages can be automatically detected and thus algorithmic promotion may be possible.

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Understanding the Behavior of Filipino Twitter Users during Disaster

Cited by 5 publications

References 0 publications

Disaster analysis through tweets

Disaster analysis through tweets

Understanding public opinion in different disaster stages: a case study of Hurricane Irma

The Signals and Noise

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