Abstract: There is general consensus that subsurface wastewater injections associated with unconventional oil and gas operations are responsible for the rapid increase of earthquake activity in the mid-U.S. Understanding the public response to these earthquakes is crucial for policy decisions that govern developing situational awareness and addressing perceived risks. However, we lack sufficient information on the reactive and recovery response behavior of the public tending to occur in the spatiotemporal vicinity of these events. Here, we review the spatiotemporal distribution of public response to the September 3, 2016, M5.8 earthquake in Pawnee, Oklahoma, USA, via a social media network (Twitter). Our findings highlight a statistically significant correlation between the spatial and temporal distribution of public response; and suggest the possible presence of a spatial distance decay, as well as a temporal far-field eect. Understanding the underlying structure of these correlations is fundamental to establishing deliberate policy decisions and targeted response actions.

Abstract: This exploratory study examines how fire and police departments used online media during the 2012 Hurricane Sandy and how these media can be used to affect trust with members of the public during such an event. Using trust theory, we describe how online communications provide a means for emergency responders to appear trustworthy through online acts of ability, integrity, and benevolence. We conclude with implications and recommendations for emergency response practice and a trajectory of future work.

Abstract: Recent research has identified a correlation between increasing Twitter activity and incurred damage in disasters. This research, however, fails to account for localized emergencies occurring in areas in which people have lost power, otherwise lack internet connectivity, or are uncompelled to Tweet during a disaster. In this paper, we analyze the correlation between daily Tweet counts and FEMA Building Level Damage Assessments during Hurricane Harvey. We find that the absolute deviation of Tweet counts from steady state is a potentially useful tool for the evolving information needs of emergency responders. Our results show this to be a more consistent and persistent metric for flood damage across the full temporal extent of the disaster. This shows that, when considering the varied information needs of emergency responders, social media tools that seek to identify emergencies need to consider both where Tweet counts are increasing and where they are dropping off.

Abstract: This paper presents the strategy and operational tools developed and implemented at the Euro-Mediterranean Seismological Centre (EMSC) to detect and diagnose the impact of global earthquakes within minutes by combining « flashsourcing » (real time monitoring of website traffic) with social media monitoring and crowdsourcing.
This approach serves both the seismological community and the public and can contribute to improved earthquake response. It collects seismological observations, improves situation awareness from a few tens of seconds to a couple of hours after earthquake occurrence and is the basis of innovative targeted real time public information services.
We also show that graphical input methods can improve crowdsourcing tools both for the increasing use of mobile devices and to erase language barriers. Finally we show how social network harvesting could provide information on indirect earthquake effects such as triggered landslides and fires, which are difficult to predict and monitor through existing geophysical networks.

Abstract: Emergency managers continue to struggle with a lack of staff, information processing tools, and sufficient trust in citizen-reported information to coordinate the use of social media in their communities. To understand possibilities for overcoming these barriers, we conduct interviews with emergency managers using scenarios describing the projective activities of community volunteers disseminating and monitoring social media. We find that coordinating social media use in communities requires alignments with local incident management systems and, in particular, existing sociotechnical infrastructure for managing citizen-reported information. These alignments open limited roles for community volunteers, notably coordinating the redistribution of official information; and stand to reshape the workflows and infrastructures of incident management systems by pushing emergency dispatchers to proactively process indirect reports of incidents obtained on social media, and integrate tools that can access hyperlocal data, curate incident and situational reports, and facilitate sensemaking among officials confronted with multiple information sources.

Abstract: The use of social media during disasters has received increasing attention in studies of the past few years. Existing research is mostly focused upon analyzing text-based messages from social media platforms such as Twitter, while image-based platforms have not been extensively addressed hitherto. However, pictures taken on-the-ground can offer reliable and valuable information for improving situation awareness and could be used as proxy indicators for relevance. To test this hypothesis, this work explores various social media platforms, including image- and text-based ones in the case of floods in Saxony 2013, Germany. Results show that there is a significant association between disaster-related messages containing images and their proximity to the disaster event. Hence, the existence of an image within a social media message can serve as an indicator for high probability of relevant content, and thus can be used for enhancing information extraction from social media towards improving situation awareness.

Abstract: In the past year, numerous weather-related disasters have continued to display the critical importance of crisis management and response. Volunteered geographic information (VGI) has been previously shown to provide illumination during all parts of the disaster timeline. Alas, for a geospatial area, the amount of data provided can cause information overload, and be difficult to process/visualize. This work presents a set of open-source tools that can be easily configured, deployed and maintained, to leverage data from Twitter's streaming service. The user interface presents data in near real-time, and allows for dynamic queries, visualizations, maps and dashboards. Another VGI challenge is quantifying trustworthiness of the data. The presented work shows integration of a Twitter-bot assessment service, which uses several heuristics to determine the bot-ness of a Twitter account. Architecture is described, Twitter data from a major metropolitan area is explored using the tools, and conclusions/follow-on work are discussed.

Abstract: In this paper we leverage the power of citizen supplied data. We examined how both physical weather sensor data (obtained from the weather underground API) and social media data (obtained from Twitter) can serve to improve local community awareness during a severe weather event. A local tornado warning was selected due to its small scale and isolated geographic area, and only Twitter data found from within this geo-locational area was used. Our results indicate that during a severe weather event, an increase in weather activity obtained from the local weather sensors does correlate with an increase in local social media usage. The data found on social media also contains additional information from, and about the community of interest during the event. While this study focuses on a small scale event, it provides the groundwork for use during a much larger weather event.

Abstract: We propose a methodology to study the occurrence of fires through image posts on Flickr; crowd-sourcing information from a noisy social media dataset can estimate the presence of fires. We collect several years worth of photos and associated metadata using fire-related search terms. We use an image classification model to detect geotagged photos that are further analysed to determine if a fire event did occur at a particular time and place. Furthermore, a case study investigates image features and spatio-temporal elements in the metadata, as well as location information contained in camera EXIF data.

Abstract: Crowdsourcing is an increasingly popular approach for processing data in response to disasters. While volunteer crowdsourcing may suÿce for high-profile disasters, paid crowdsourcing may be necessary to recruit workers for less prominent events. Thus, understanding the impact of payment schemes on worker behavior and motivation may improve outcomes. In this work, we presented workers recruited from Amazon Mechanical Turk with a disaster response task in which they could provide a variable number of image ratings. We paid workers a fixed amount to provide a minimum number of image ratings, allowing them to voluntarily provide more if desired; this allowed us to examine the impact of dierent amounts of required work. We found that requiring no ratings resulted in workers voluntary completing more work, and being more likely to indicate motivation related to interest on a post survey, than when small numbers of ratings were required. This is consistent with the motivational crowding-out eect, even in paid crowdsourcing. We additionally found that providing feedback on progress positively impacted the amount of work done.

Abstract: Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.

Abstract: During large earthquakes, it is critical to safely guide evacuation efforts and to prevent accidents caused by congestion. In this paper, we focus on detecting the degree of crowd congestion following an earthquake based on information posted to Social Networking Services (SNSs). This research uses text data posted to Twitter just before/after the occurrence of the Great East Japan Earthquake (11 March 2011 at 02:46 PM JST). First, we extract co-occurring place names, proper nouns, and time-series information from tweets about congestion in the Tokyo metropolitan area (TMA). Next, using these extracted data, we analyze the frequency and spatiotemporal characteristics of these tweets. Finally, we identify expressions that describe the degree of crowd congestion and discuss methods to quantify these expressions based on a questionnaire survey and tweets that contain a photograph.