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Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awarenes...
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... what types of information or results they would expect to be able to share with colleagues if they were to use an application like SensePlace2. This poll revealed that printable maps (97%), a link that would launch the application with preloaded data (63.6%), pre-formatted text reports (51.5%) and static screen captures (51.5%) were preferred (Fig. ...
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... Although it's easy to gain a sampling view of X using the API, accessing the entire view is more challenging. We can only access 1% of the Twitter data through the APIs, and recent issues have been expressed in [39] regarding the sampling process and the quality of the Twitter data obtained through the API. ...
Most of the information is open to the public and can be mined. Huge amounts of data are available on the social networking site like Twitter. This data may be unstructured, semi-structured, or structured in any combination. This enables users to discuss concerns with various communities, as well as to share and express their opinions about various subjects. Sentiment analysis of Twitter data has been the subject of extensive research. Sentiment: that describes how people feel or think about a specific subject. Not facts, but subjective perceptions of the domain are the result. Twitter is one of the most widely used platforms. Many scholars who conduct research in critical domains such as consumer brands, market performance, and democratic election prediction have found inspiration from Twitter. The initiative focuses on Twitter data analysis. This paper focuses on a tool to assess public sentiment, monitor crises, conduct market research, and the influence and reach of a Twitter trend, it lets users view a range of metrics related to any hashtag, such as the most popular users utilizing it, users with the greatest reach, an estimate of the hashtag's actual and maximum reach, the timely frequency of tweets and retweets, and the estimated market value of the hashtag with the use of python and Twitter API. It will help combat misinformation and online negativity and promote accountability with a focus on visual content analysis.
... Past studies have confirmed the critical role of the Internet as a communication platform among individuals and organizations in times of crises (Jefferson 2006). Among the existing platforms, X has emerged as one that affords users immediate updates about ongoing situations, to express opinions and feelings, and to appeal for specific actions related to certain crises (Heverin and Zach 2010;Hughes and Palen 2009;MacEachren et al. 2011). X has also been used as a tool to monitor the evolving status and track the mood of the population affected by a crisis (Doan, Vo, and Collier 2012;MacEachren et al. 2011). ...
... Among the existing platforms, X has emerged as one that affords users immediate updates about ongoing situations, to express opinions and feelings, and to appeal for specific actions related to certain crises (Heverin and Zach 2010;Hughes and Palen 2009;MacEachren et al. 2011). X has also been used as a tool to monitor the evolving status and track the mood of the population affected by a crisis (Doan, Vo, and Collier 2012;MacEachren et al. 2011). Furthermore, past studies (Gaffney 2010;Kavanaugh et al. 2011;Starbird et al. 2010) found that X experiences a rapid growth in traffic volume during and after crises such as natural disasters or political protests. ...
This article examined how social media content has shaped the representation of countries for publics around the world. Based on world system theory (WST), the study investigated the underlying predictors of country mentions on X in 2018. It confirmed that countries with greater economic power-or higher status in the world system-received more mentions. Furthermore, countries with larger populations were mentioned more frequently as were countries that experienced major conflicts. The findings yielded from structural equation modeling shed new light on the interrelationships that drive the social media representation of individual countries and delineated an integrated Model of Country Mentions on X. These findings have implications for global leaders, policy makers, and social media firms to consider.
... For instance, numerous visualization tools have been designed with a primary focus on showcasing sentiment analysis of tweets, as exemplified by [1][2][3][4][5][6][7][8][9][10]. Conversely, alternative visualization tools concentrate on the analysis and exploration of tweet data based on its geo-spatial information, as demonstrated by [11][12][13][14][15][16][17]. The COVID-19 outbreak initially surfaced in news reports during the month of December 2019. ...
In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.
... Additionally, social media can provide real-time information and serve as a social sensing tool. For example, many studies have shown that citizen plays role as a sensor in the response to natural disasters such as earthquake (Sakaki et al., 2010;MacEachren et al., 2011), Hurricane (Ukkusuri et al., 2014), fire (Craglia et al., 2012;Wang, Ye, & Tsou, 2016), or floods (Fuchs et al., 2013;Fohringer et al., 2015;Li et al., 2018), informing recover and emergency response efforts. ...
... In this research, we employ TextBlob, a python library, for sentiment analysis. This lexicon-based method assigns sentiment values as polarity ranging from -1 to 1, with 1 being the most positive, 0 representing neutral sentiment, and -1 indicating the most negative sentiment (Loria et al., 2018). ...
Research shows that certain external factors can affect the mental health of many people in a community. Moreover, the importance of mental health has significantly increased in recent years due to the COVID-19 pandemic. Many people communicate and express their emotions through social media platforms, which provide researchers with opportunities to examine insights into their opinions and mental state. While social sensing studies using social media data have flourished in the last decade, many studies using social media data to detect and predict mental health status have focused on the individual level. In this study, we aim to generate a social sensing index for mental health to monitor emotional well-being, which is closely related to mental health, and to identify daily trends in negative emotions at the city level. We conduct sentiment analysis on Twitter data and compute entropy of the degree of sentiment change to develop the index. We observe sentiment trends fluctuate significantly in response to unusual events. It is found that the social sensing index for mental health reflects both city-wide and local events that trigger negative emotions, as well as areas where negative emotions persist. The study contributes to the growing body of research that uses social media data to examine mental health at a city-level. We focus on mental health at the city-level rather than individual, which provides a broader perspective on the mental health of a population. Social sensing index for mental health allows public health professionals to monitor and identify persistent negative sentiments and potential areas where mental health issues may emerge.
... One of the map-based apps that crisis managers use to examine the crisis's spatial and temporal progression while filtering data gathered from people is SensePlace2. [34][35][36][37][38]. ...
In the past three years, social media has had a significant impact on our lives, including crisis management. The COVID-19 pandemic highlighted the importance of accurate information and exposed the spread of false information. This paper specifically examines the COVID-19 crisis and analyzes relevant literature to provide insights for national authorities and organizations. Utilizing social media data for crisis management poses challenges due to its unstructured nature. To overcome this, the paper proposes a comprehensive method that addresses all aspects of long-term crisis management. This method relies on labeled and structured information for accurate sentiment analysis and classification. An automated approach is presented to annotate and classify tweet texts, reducing manual labeling and improving classifier accuracy. The framework involves generating topics using Latent Dirichlet Allocation (LDA) and ranking them with a new algorithm for data annotation. The labeled text is transformed into feature representation using Bert embeddings, which can be utilized in deep learning models for categorizing textual data. The primary aim of this paper is to offer valuable insights and resources to researchers studying crisis management through social media literature, with a specific focus on high-accuracy sentiment analysis.
... Previous work has shown how geo-tagged social media data reflects the spatiotemporal distribution of social groups (MacEachren et al. 2011). For example, scatterplots combine event detection and classification in investigating the geotagged social media, to enable situation awareness (Thom et al. 2012;Cao et al. 2012) for geospatial information diffusion. ...
... The works above addressed the uncertainty from the perspective of the producer of VGI and the trustworthiness of the users on the systems. During the last few years, the study of uncertainty and its propagation through the visual analytics workflow have gained popularity. Early, in 2015, MacEachren proposed to consider the propagation of uncertainty through the whole VA workflow rather than just the visualization of the uncertainty at the end of the pipeline (MacEachren et al. 2011). He illustrated current challenges and possible approaches to tackle uncertainty using definitions from decision sciences. ...
Combining data from different sources and modalities can unlock novel insights that are not available by analyzing single data sources in isolation. We investigate how multimodal user-generated data, consisting of images, videos, or text descriptions, can be used to enrich trajectories of migratory birds, e.g., for research on biodiversity or climate change. Firstly, we present our work on advanced visual analysis of GPS trajectory data. We developed an interactive application that lets domain experts from ornithology naturally explore spatiotemporal data and effectively use their knowledge. Secondly, we discuss work on the integration of general-purpose image data into citizen science platforms. As part of inter-project cooperation, we contribute to the development of a classifier pipeline to semi-automatically extract images that can be integrated with different data sources to vastly increase the number of available records in citizen science platforms. These works are an important foundation for a dynamic matching approach to jointly integrate geospatial trajectory data and user-generated geo-referenced content. Building on this work, we explore the joint visualization of trajectory data and VGI data while considering the uncertainty of observations. BirdTrace , a visual analytics approach to enable a multi-scale analysis of trajectory and multimodal user-generated data, is highlighted. Finally, we comment on the possibility to enhance prediction models for trajectories by integrating additional data and domain knowledge.
... There exists a rich amount of literature on leveraging social media, especially Twitter, for supporting disaster response and situational awareness (Crooks et al. 2013;De Longueville et al. 2009;Feng et al. 2022;Imran et al. 2015;MacEachren et al. 2011;Murthy and Longwell 2013;Starbird and Stamberger 2010). Social media provides near real-time information about the situation on the ground after a disaster (Hu and Wang 2020), which makes it a valuable alternative information source for emergency managers. ...
... There are logical-conceptual, creative, conscious, and unconscious elements in the dynamic reflection that enable people to create mental models of an external event (Bedny & Meister, 1999;Endsley & Kiris, 1995;Zhao & Rosson, 2009). Several studies have investigated the application of social media as a tool to examine situational awareness during catastrophic events (Bedny & Meister, 1999;MacEachren et al., 2011;. Switzer and Vedlitz (2017) studied the relationship between drought and individual awareness, examined the contributing factors, and assessed the impacts of drought awareness on the perception of risk and policy preferences. ...
... The literature shows that although a great effort has gone into developing metrics to understand the awareness (Bedny & Meister, 1999;MacEachren et al., 2011; and risk (Daniell et al., 2010;Okazawa et al., 2011;Robat Mili et al., 2018) aspect of the natural hazards, these two have not been collectively considered for assessing the risk awareness of the community against a natural hazard. Relying on the recent advances in satellite data and availability of big data from social media, this pilot study proposes a new index to simultaneously monitor the level of awareness and risk perception of residents in southeast Texas against the strong wind and violent heavy rainfall of the Hurricane Harvey. ...
This research dissertation focuses on the growing challenges faced by disaster management in the face of increased frequency and intensity of natural disasters caused by climate change. Specifically, it explores the role of social media platforms, particularly Twitter, as crucial tools for disseminating information during such disasters. By conducting a spatiotemporal social media data analysis of three different natural disaster cases, the study aims to gain a deeper understanding of how social media can contribute to effective disaster management.
... Billboard selection [98] Store selection [99] House selection [99,100] Warehouse selection [101] POI Marathon monitoring [105] Crime pattern analysis [96,[106][107][108] Resource allocation [32,109,110] Fire station selection [48] Crisis management [111] Disease analysis [95,106,112,113] Surveillance video inspection [114] Natural disaster Flood impact analysis [115] Evacuation monitoring and understanding [116] Architecture Understanding Human-scale scene sense-making [117,118] Non-visual city attribute prediction [119] Shadow distribution analysis [120] Location functionality analysis [121] 3D environment exploration [122,123] Planning Impact analysis of new buildings [120,124] Urban space design precedent seeking [125] Economics Real estate market understanding [126] Spatiotemporal understanding of sales [25] Trade network analysis [127] Economic influence between countries [86] Public service ...
... Visual analytics also supports in-depth later analysis for public security, in areas such as crime analysis [96,[106][107][108], police resource allocation [32,109,110], fire station selection [48], crisis management [111], disease analysis [95,106,112,113], and surveillance video inspection [114]. For example, given historic crime data, crime hotspots with frequent occurrences of crime can be visually determined [106][107][108], and urban experts can take informed action such as appropriate policing. ...
... A wordle or word cloud [159] is usually used in text-based urban visual analytics [14,19,102,104,111]. It assists in inspecting massive amounts of text information, usually combined with keyword extraction. ...
Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.
... Previous studies (Abel et al., 2012;Ashktorab et al., 2014;Caragea et al., 2011;Imran et al., 2014;MacEachren et al., 2011;Purohit & Sheth, 2013;Sheth et al., 2010;Vieweg et al., 2010;Yin et al., 2015) have shown that information extracted out of the tweets which are posted publicly on the Twitter platform contribute to better understanding of an event, as it unfolds, and also provides a direction to develop actionable plans (Antoniou & Ciaramicoli, 2013). Twitter possesses real-time and extremely informal communication characteristics. ...
Twitter, a microblogging platform, receives real-time information via informal conversations, and it has, accordingly, become the main source of data for research studies based on emergency situational awareness. Millions of tweets are posted on Twitter every day, and during disasters, the frequency of tweets relating to an on-going crisis event grows exponentially. This unprecedented increase in the number of tweets during disasters needs to be monitored, identified, processed, and analyzed so that necessary measures can be taken at the earliest to reduce the loss or damage during emergencies. However, due to large voluminous data being available during crisis hours, it is almost impossible for a human to perform these tasks in real time. In this regard, a semi-automated AI-based disaster response system for Twitter data is proposed. The proposed disaster response system would be capable of extracting essential situational awareness information related to a disaster and would also be capable of sketching tentative area of critically affected population.
