Analyzing the spread of tweets in response to Paris attacks

Cvetojevic, Sreten; Hochmair, Hartwig H.

doi:10.1016/j.compenvurbsys.2018.03.010

Cited by 41 publications

(30 citation statements)

References 33 publications

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“…In fact, recent studies have found that OSNs play an important role during crisis events as a channel where people seek and share information to make sense of the situation, as well as organize themselves and look for help. For example, the use of online social media in crisis events has been investigated during natural disasters such as the 2009 Oklahoma grass-fires (Vieweg et al 2010), the 2009 Marseille fire (De Longueville et al 2009), or the 2011 and 2013 floods in Brisbane (Hung et al 2016), as well as in terror attacks such as the 2013 Boston Marathon Bombing (Guo 2017) and the 2015 terror attacks in Paris (Cvetojevic and Hochmair 2018), or the riots during the 2017 G20 summit in Hamburg (Kušen and Strembeck 2018a).…”

Section: Introductionmentioning

confidence: 99%

An analysis of emotion-exchange motifs in multiplex networks during emergency events

Kušen

Strembeck

2019

Appl Netw Sci

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In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchange motifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension.

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Section: Introductionmentioning

confidence: 99%

An analysis of emotion-exchange motifs in multiplex networks during emergency events

Kušen

Strembeck

2019

Appl Netw Sci

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“…Figure 10 shows the results. We can find that in the study area, the morning rush hours (08-10) and the evening rush hours (17)(18)(19)(20) have the strongest human response intensity. During these two periods, important transportation hubs (Commercial business center, large jobs-housing area) and water ponding point areas have become regions with a high response in the main urban area of Beijing.…”

Section: Human Response At Grid Scalementioning

confidence: 82%

“…Unfortunately, the dynamic changes of the human response to a disaster are extremely important in disaster evaluation and mitigation.The emerging social media data have shown their usefulness in tracking human behaviors in response to natural disasters and emergencies. Nowadays, Twitter, Facebook, Flicker and Weibo have become indispensable platforms for people to share their ideas and disseminate vital information in time [14][15][16], in particularly when a natural disaster, public safety event, or disease infection event occurs [17][18][19][20][21]. Data collected through these platforms have been used in improving situation awareness [22][23][24], event detection [25,26], communication analysis [27,28] and even helping governments guide the public opinions [29,30].Social media data have also been used to detect and monitor the ongoing development of disasters such as influenza transmission, floods, typhoons, hurricanes, and terrorist attacks [31][32][33][34].…”

mentioning

confidence: 99%

Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Wang

Liang

et al. 2019

Applied Sciences

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Natural disasters cause significant casualties and losses in urban areas every year. Further, the frequency and intensity of natural disasters have increased significantly over the past couple of decades in the context of global climate change. Understanding how urban dwellers learn about and response to a natural hazard is of great significance as more and more people migrate to cities. Social media has become one of the most essential communication platforms in the virtual space for users to share their knowledge, information, and opinions about almost everything in the physical world. Geo-tagged posts published on different social media platforms contain a huge amount of information that can help us to better understand the dynamics of collective geo-tagged human activities. In this study, we investigated the spatiotemporal distribution patterns of the collective geo-tagged human activities in Beijing when it was afflicted by the “6-22” rainstorm. We used a variety of machine learning and statistical methods to examine the correlations between rainstorm-related microblogs and the rainstorm characteristics at a fine spatial and a fine temporal scale across Beijing. We also studied factors that could be used to explain the changes of the rainstorm-related blogging activities. Our results show that the human response to a disaster is very consistent, though with certain time lags, in the virtual and physical spaces at both the grid and city scales. Such a consistency varies significantly across our study area.

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“…Hashtags have been widely used in tracking topical information during and after crisis situations. For example, Twitter hashtags related to the Paris attacks in November 2015 helped to model the propagation of information shared across the globe in the Twitter network [23]. Spatial and temporal characteristics of the Twitter feed were also used as a sensor network to assess the extent and impact of earthquakes, such as the 2011 earthquake in Christchurch (New Zealand) [24], or the Napa (California, USA) earthquake in 2014 [25].…”

Section: Related Literaturementioning

confidence: 99%

Using Twitter to Analyze the Effect of Hurricanes on Human Mobility Patterns

2019

Self Cite

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Understanding human mobility patterns becomes essential in crisis management and response. This study analyzes the effect of two hurricanes in the United States on human mobility patterns, more specifically on trip distance (displacement), radius of gyration, and mean square displacement, using Twitter data. The study examines three geographical regions which include urbanized areas (Houston, Texas; Miami-Dade County, Florida) and both rural and urbanized areas (North and South Carolina) affected by hurricanes Matthew (2016) and Harvey (2017). Comparison of movement patterns before, during, and after each hurricane shows that displacement and activity space decreased during the events in the regions. Part of this decline can be potentially tied to observed lower tweet numbers around supply facilities during hurricanes, when many of them are closed, as well as to numerous flooded and blocked roads reported in the affected regions. Furthermore, it is shown that displacement patterns can be modeled through a truncated power-law before, during, and after the analyzed hurricanes, which demonstrates the resilience of human mobility behavior in this regard. Analysis of hashtag use in the three study areas indicates that Twitter contributors post about the events primarily during the hurricane landfall and to some extent also during hurricane preparation. This increase in hurricane-related Twitter topics and decrease in activity space provides a tie between changed travel behavior in affected areas and user perception of hurricanes in the Twitter community. Overall, this study adds to the body of knowledge that connects human mobility to natural crises at the local level. It suggests that governmental and rescue operations need to respond to and be prepared for reduced mobility of residents in affected regions during natural crisis events. disasters including wildfires, earthquakes, floods, storms, tsunamis, and hurricanes [6,7]. As expected, the frequency of disaster related tweets is highest in the spatial proximity of a disaster [8,9].Related work on movement analysis focuses primarily on the effects of crisis events on a larger scale and longer-term movements. One study, for example, explored evacuation patterns by leveraging user location information from tweets posted in the hours prior and concurrent to Hurricane Matthew in 2016 [10], and another study used Twitter data to estimate the percentage of evacuees during the same hurricane [11]. Tweets were also used to identify refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015 [12].Despite such analysis, the effect of natural disasters on local mobility patterns (e.g., for the population remaining in the affected regions during such an event), is less explored. Furthermore, the implications of changed mobility patterns on planning and rescue operations need to be addressed. As a step in this direction, the goal of this study is to investigate changes in human activity patterns du...

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Analyzing the spread of tweets in response to Paris attacks

Cited by 41 publications

References 33 publications

An analysis of emotion-exchange motifs in multiplex networks during emergency events

An analysis of emotion-exchange motifs in multiplex networks during emergency events

Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

Using Twitter to Analyze the Effect of Hurricanes on Human Mobility Patterns

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