Finding Eyewitness Tweets During Crises

Lubold, Nichola; Pon-Barry, Heather; Liu, Huan

doi:10.3115/v1/w14-2509

Cited by 32 publications

(29 citation statements)

References 8 publications

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“…The problem of recognizing eyewitness tweets was independently investigated in [13]. While the authors evaluated whether linguistic features could be used to identify such tweets, here we analyzed several kinds of behavioral patterns in tweets from crisis regions.…”

Section: Related Workmentioning

confidence: 99%

A behavior analytics approach to identifying tweets from crisis regions

Kumar

Liu

2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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The growing popularity of Twitter as an information medium has allowed unprecedented access to first-hand information during crises and mass emergency situations. Due to the sheer volume of information generated during a disaster, a key challenge is to filter tweets from the crisis region so their analysis can be prioritized. In this paper, we introduce the task of identifying whether a tweet is generated from crisis regions and formulate it as a decision problem. This problem is challenging due to the fact that only ∼1% of all tweets have location information. Existing approaches tackle this problem by predicting the location of the user using historical tweets from users or their social network. As collecting historical information is not practical during emergency situations, we investigate whether it is possible to determine that a tweet originates from the crisis region through the information in the tweet and the publishing user's profile.

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Section: Related Workmentioning

confidence: 99%

A behavior analytics approach to identifying tweets from crisis regions

Kumar

Liu

2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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“…The rise of Online Social Networks (OSN) has facilitated a wide application of its data as sensors for information to solve different problems. For example, Twitter data has been used for predicting election results, detecting the spread of flu epidemics, and a source for finding eye-witnesses during criminal incidents and crises [1], [2]. This phenomenon is possible due to the great overlap between our online and offline worlds.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Nouh

Nurse

Goldsmith

2019

2019 IEEE International Conference on Intelligence and Security Informatics (ISI)

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The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is threefold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.

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“…There is also potential with this kind of data to learn more about various other spatio-temporal patterns, for example, of biodiversity conservation activities (Di Minin et al, 2015). Knowing the location from which a user is tweeting is also useful to gauge the value of the data she is producing (Morstatter et al, 2014). In addition, the location information allows comparison of Twitter data with other, 'offline' data, for example, socio-demographic variables from censuses or surveys, health data, geographic information, environmental data, etc.…”

Section: Introductionmentioning

confidence: 99%

Archiving information from geotagged tweets to promote reproducibility and comparability in social media research

2017

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Sharing social media research datasets allows for reproducibility and peer-review, but it is very often difficult or even impossible to achieve due to legal restrictions and can also be ethically questionable. What is more, research data repositories and other research infrastructure and research support institutions are only starting to target social media researchers. In this paper, we present a practical solution to sharing social media data with the help of a social science data archive. Our aim is to contribute to the effort of enhancing comparability and reproducibility in social media research by taking some first steps towards setting standards for sustainable data archiving. We present a showcase for sharing social media data with the example of a big dataset containing geotagged tweets (several months of continued geotagged tweets from the United States from 2014 and 2015; nearly half a billion tweets in total) through a research data archive. We provide a general background to the process of long-term archiving of research data. After some consideration of the current obstacles for sharing and archiving social media data, we present our solution of archiving the specific dataset of geotagged tweets at the GESIS Data Archive for the Social Sciences, a publicly funded German data archive for secure and long-term archiving of social science data. We archived and documented tweet IDs and additional information to improve reproducibility of the initial research while also attending to ethical and legal considerations, and taking into account Twitter's terms of service in particular.

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Finding Eyewitness Tweets During Crises

Cited by 32 publications

References 8 publications

A behavior analytics approach to identifying tweets from crisis regions

A behavior analytics approach to identifying tweets from crisis regions

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Archiving information from geotagged tweets to promote reproducibility and comparability in social media research

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