Why I Retweet? Exploring User’s Perspective on Decision-Making of Information Spreading during Disasters

The extensive use of social media during disasters raises an important issue concerning use of social media to spread information, including misinformation. This study explores the underlying behavioral context of disaster information sharing by Twitter users. We conducted a web survey with 999 respondents in Japan to determine what makes people retweet disaster information in disaster situations. As a result of factor analysis, four factors were identified from 36 questions, namely: 1) Willingness to provide relevant and updated information because the information is believable, 2) Want people to know the information they perceive as important, 3) Retweeter subjective feelings and interests, and 4) Want to get feedback and alert other people. The results suggest that two of the factors influenced different groups of people in the community differently; however, everybody can play their role to reduce the negative impact of social media used for future disaster. Based on the findings, we discuss practical and design implications of social media use during disasters

Toward Extended Situational Crisis Communication Theory: Include Visuals, Prior Performance, and Framing Devices

Human brains are inherently capable of receiving and processing visual messages faster than written text messages. The recent proliferation of internet use, social media platforms, smartphones, and online news media sites facilitated the spread of visual content (e.g., pictures, videos, and data visualizations) online much higher than before. However, visual contents have been largely ignored in crisis communication research, leaving the crisis managers to devise strategic crisis responses and deal with a crisis without sufficient research evidence. Responding to a recent research call to fill the gap, this dissertation conducts a 2 (picture: action vs. damage) × 3 (distinctiveness: high vs. low vs. no) between-subject experimental design, informed by attribution theory (AT) and situational crisis communication theory (SCCT). This online experiment aims to see the effects of pictures and an organization\u27s distinctiveness (i.e., an organization\u27s prior good or bad performance) on people\u27s crisis reactions in a real oil-spill crisis phenomenon and how both the pictures and the distinctiveness interact with each other. The effects were tested on people\u27s five reactions: a) crisis responsibility, b) negative emotion, c) negative word of mouth, d) punitiveness, and e) purchase intention. Visual stimuli manipulation was created using pictures relating to actions (e.g., cleaning spilled oil) and damages (e.g., dolphin carcass). Distinctiveness stimuli manipulation was created using written texts relating good or bad performance in the past. Simple effect results show that the damaging pictures invoke significantly higher negative emotion among participants and their higher punitiveness toward the company than the action pictures. At the same time, the crisis-hit company\u27s prior bad performance information (i.e., low distinctiveness), compared to its prior good performance (i.e., high distinctiveness), leads to people\u27s higher crisis responsibility, higher negative emotion, higher negative word of mouth, higher punitiveness, and lower purchase intention toward the company. There are significant interaction effects between picture and distinctiveness. In other words, the distinctiveness effects are moderated by or depend on the levels of pictures. The results contributed to the crisis communication literature by offering evidence supporting visual effects on people\u27s perceptions in a crisis and the roles of framing devices in both visual and textual content in the SCCT model. The insights are provided in the contexts of a social media platform and a real crisis. Overall, this dissertation proposed an extension of the SCCT model offering a more in-depth understanding of a crisis and its management, which is not adequately explained in the old model. Based on the insights, the study also offered practical implications for crisis communication practitioners and future research directions in visual crisis communication

Master of Science

thesisDuring rapid-onset disasters, timely dissemination of warning information to the public is crucial. Official emergency information channels are often slow, leaving the public to monitor social media websites for more timely updates. Examining Twitter communications, or tweets, sent during the 2012 Waldo Canyon Fire, this research seeks to determine what level of descriptive information is sent through Twitter during a wildfire, whether or not that information can inform other users of changes in fire activity, and how the spatial and temporal information within a tweet can be used in conjunction with geographic information systems (GIS) to determine fire location and activity. This research utilized geotagged tweets and viewshed analysis in GIS as a means of determining what portions of the wildfire are visible from each Twitter user. These visible areas, or viewsheds, were then overlapped with viewsheds from other users to generate shared viewsheds. Both individual and shared viewsheds were compared to the area of new fire growth to determine if burning areas could be more confidently identified by considering different user perspectives. The shared viewshed method showed that while increasing the number of observations does result in a decrease in shared visible area, the portion of the shared viewshed that falls within the fire boundary significantly increases. Many groupings, iv which were compiled based on time sent and ranged in size from two to eight tweets, could see more than 20% of the fire. This research found that there is the potential for users to inform one another of changes in fire activity that may not be visible from different points of view. The addition of viewshed analysis adds another layer of valuable information to the tweets and could be useful if done in real-time, especially during events occurring at a smaller scale

Spatiotemporal Variation in Emotional Responses to 2017 Terrorist Attacks in London Using Twitter Data

Terrorist attacks have a significant impact on human lives. This study examined emotional responses after the terrorist attacks in London in March and June of 2017, respectively. This research extracted tweets related to the two attacks by developing a Python tool interacting with the Twitter Application Program Interface (API). The tweets were analyzed for its negative emotion expression such as sadness. This study then analyzed these negative tweets using the space-time permutation model in SatScan and assessed their variation in space and time. Results suggested two significant clusters of negative tweets after the first attack. These clusters located in the metropolitan area of London and between Manchester and Liverpool within ten days of the attack. The findings may contribute to quick surveillance of emotional responses on the Twitter users

Pod travel in the pandemic era: social media analytics on travel sentiment and spatial distribution

[EMBARGOED UNTIL 5/31/2023] The outbreak of the COVID-19 pandemic brought an enormous downturn to the overall hospitality and tourism industry. Accordingly, a novel trend emerged, called 'pod travel,' reflecting travelers' needs and behavior shifts. This study aims to understand pod travel through two phases. In study 1, pod travel is conceptualized using the text mining approach of Twitter by comparing two time periods: Jan 2019 (before COVID-19) and Jan 2021 (during COVID-19) to figure out distinguishable differences that came out of the pandemic. Specifically, several theoretical concepts, including chaos theory, travel resilience, optimism bias, and xenophobia were adopted to understand the phenomenon, and through a topic modeling approach. In study 2, social media analytics on Twitter is implemented to identify public opinion about the pandemic and their sentiments and spatial distributions of the United States for travel. The findings are expected to deliver meaningful behavioral aspects distinguished from past traveler behavior. In addition to enlightening industry practitioners to overcome this hardship and behave strategically, this study proposes sustainable pod travel that is expected to be more prevalent and prosper.Includes bibliographical references

Finetuning BERT and XLNet for Sentiment Analysis of Stock Market Tweets using Mixout and Dropout Regularization

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large database created by twitter stock market sentiment analysis has always been the subject of interest for various researchers, investors, and scientists due to its highly unpredictable nature. Sentiment analysis can be performed in different ways, but the focus of this study is to perform sentiment analysis using the transformer-based pre-trained models such as BERT(bi-directional Encoder Representations from Transformers) and XLNet which is a Generalised autoregressive model with fewer training instances using Mixout regularization as the traditional machine and deep learning models such as Random Forest, Naïve Bayes, Recurrent Neural Network (RNN), Long short-term memory (LSTM) because fails when given fewer training instances and it required intense feature engineering and processing of textual data. The objective of this research is to study and understand the performance of BERT and XLNet with fewer training instances using the Mixout regularization for stock market sentiment analysis. The proposed model resulted in improved performance in terms of accuracy, precision, recall and f1-score for both the BERT and XLNet models using mixout regularization when given adequate and under-sampled data

Crime Reporting Through Social Media: Potential Opportunities in Community Policing

The popularity and usage of social media over the years has increased. Due to this increase there has now been an influx of information shared on a global platform. This information that has been shared can be as superficial as daily activities, food eaten or as sensitive as graphic crimes committed. The Perceived Social Media Anonymity Effect is a concept that I am introducing and seek to explore. It is based on the premise that allows one to relinquish the fear of being in large crowds and speaking up when crimes have been committed while also being able to seek solitude among numbers on a social media platform where it appears easier to report and inform. This concept stems from the Bystander Effect. Darley & Latane (1968) states that the Bystander Effect refers to the phenomenon surrounding the passivity of onlookers’ willingness to help or intervene when faced with critical situations where others are being harmed. This study reviewed literature and high-profile social media exposure cases and analyzed the following questions: To what extent is there a nexus between non-reporting of crimes and reporting on social media? Furthermore, what are the perceived factors an individual reports that they take into account when determining whether to post or share videos of crimes on social media platforms and/or not reporting to police? To explore these issues, the public cases of Eric Garner, Laquan McDonald and Kenneka Jenkins were used to determine the impact of social media and its usage in a way of spreading information to the general public and at times used as a catalyst for social change. Information from a range of sources including local and national newspaper articles, media interviews, Chicago Police Department and the New York Police Department are synthesized and analyzed. This study concludes by reviewing implications and findings and recommendations for future study. The popularity and usage of social media over the years has increased. Due to this increase there has now been an influx of information shared on a global platform. This information that has been shared can be as superficial as daily activities, food eaten or as sensitive as graphic crimes committed. The Perceived Social Media Anonymity Effect is a concept that I am introducing and seek to explore. It is based on the premise that allows one to relinquish the fear of being in large crowds and speaking up when crimes have been committed while also being able to seek solitude among numbers on a social media platform where it appears easier to report and inform. This concept stems from the Bystander Effect. Darley & Latane (1968) states that the Bystander Effect refers to the phenomenon surrounding the passivity of onlookers’ willingness to help or intervene when faced with critical situations where others are being harmed. This study reviewed literature and high-profile social media exposure cases and analyzed the following questions: To what extent is there a nexus between non-reporting of crimes and reporting on social media? Furthermore, what are the perceived factors an individual reports that they take into account when determining whether to post or share videos of crimes on social media platforms and/or not reporting to police? To explore these issues, the public cases of Eric Garner, Laquan McDonald and Kenneka Jenkins were used to determine the impact of social media and its usage in a way of spreading information to the general public and at times used as a catalyst for social change. Information from a range of sources including local and national newspaper articles, media interviews, Chicago Police Department and the New York Police Department are synthesized and analyzed. This study concludes by reviewing implications and findings and recommendations for future study

Social Media and Shaping Voting Behavior of Youth: The Scottish Referendum 2014 Case

This study analyzes the role of social media in shaping voting behavior of youth in the Scottish Independence Referendum 2014. Findings from a survey of inhabitants of Scotland and England (n=985) indicate that the social media is composed of limited self- selected members (especially Facebook). Young voters seek information from like-minded political experts on social media. The politically aware young voters are more efficient and active than their less politically aware counterparts with respect to gaining political information. Social media were effective in changing voting behavior of young voters in Scottish Referendum 2014.

Twitter Narratives During Hurricane Matthew: Evaluation of Immediate Disaster Stages

The objective of this thesis is to investigate the effectiveness of foundational disaster literature using a contemporary data platform. Due to the recency of social media over the last decade, novel opportunities now exist to study disaster preparation, response, recovery, and mitigation through in-situ accounts. The author characterizes immediate disaster stages based upon overarching themes identified by Twitter users impacted by Hurricane Matthew in Savannah, Georgia. Using both quantitative and qualitative methods, the author identifies the frequency of tweets within each immediate disaster stage, as well as the context of each tweet. In addition, the author uses individual social media narratives to gauge the resident's story through the duration of Hurricane Matthew. The author's findings suggest the continuing effectiveness of foundational disaster literature through both quantities and qualitative methods. Results emphasize prior studies that address residents' narratives during a disaster event. The further incorporation of social media proves to be an additional outlet for research in the meteorological field.Sociolog