163 Hits in 4.1 sec

Weakly Supervised Classification of Tweets for Disaster Management

Girish Keshav Palshikar, Manoj Apte, Deepak Pandita
2017 European Conference on Information Retrieval  
Given this important role, real-time analysis of social media contents to locate, organize and use valuable information for disaster management is crucial.  ...  In this paper, we propose self-learning algorithms that, with minimal supervision, construct a simple bag-of-words model of information expressed in the news about various natural disasters.  ...  We use this topic-based classification scheme as a baseline because, it is also weakly supervised, like our approach.  ... 
dblp:conf/ecir/PalshikarAP17 fatcat:vryisecpajgq3a3bz6hrbwlwse

Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management

Wenlin Yao, Cheng Zhang, Shiva Saravanan, Ruihong Huang, Ali Mostafavi
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management.  ...  The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained  ...  Supplemental Material Here is the full list of keywords used for each event category (Section Event Categories and Event Keywords).  ... 
doi:10.1609/aaai.v34i01.5391 fatcat:thnrvelrffav3n653xr2i5dbfi

Microblogging during the European Floods 2013

Christian Reuter, Julian Schröter
2015 International Journal of Information Systems for Crisis Response and Management  
To ease the analysis of data we suggest a retweet ratio, which is illustrated to be higher with important tweets and may help selecting tweets for mining.  ...  Social media is becoming more and more important in crisis management.  ...  ACKNOWLEDGEMENTS The research project EmerGent' was funded by a grant of the European Union (FP7 No. 608352).  ... 
doi:10.4018/ijiscram.2015010102 fatcat:jrw6sdcj2rh7ni5fzwcpwjav5q

Classification of colloquial Arabic tweets in real-time to detect high-risk floods

Waleed Alabbas, Haider M. al-Khateeb, Ali Mansour, Gregory Epiphaniou, Ingo Frommholz
2017 2017 International Conference On Social Media, Wearable And Web Analytics (Social Media)  
Another technique combined three classifiers by computing the model scores of a weakly supervised, strongly supervised, and semi-supervised classifiers.  ...  Data labelling stage The supervised learning of classification algorithms requires manual data labelling.  ... 
doi:10.1109/socialmedia.2017.8057358 fatcat:p35p3jcxzberhhikay6mtj7shq

An Ontology-Based Approach to Social Media Mining for Crisis Management

Vanni Zavarella, Hristo Tanev, Ralf Steinberger, Erik Van der Goot
2014 Extended Semantic Web Conference  
We illustrate a number of strategies for customizing the system to process social media texts such as Twitter messages, which are currently seen as a crucial source of information for crisis management  ...  We describe an existing multilingual information extraction system that automatically detects event information on disasters, conflicts and health threats in near-real time from a continuous flow of on-line  ...  In [1] we report about an evaluation on populating a microontology for disaster management with instances of events extracted from a stream of tweets published during several big tropical storms, in  ... 
dblp:conf/esws/ZavarellaTSG14 fatcat:ofystmh5dzbvhjwloefhlra3l4

A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

Saini Jacob Soman, P Swaminathan, R Anandan, K Kalaivani
2018 International Journal of Engineering & Technology  
Sentiment analysis of tweets is a challenging task.  ...  This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages.  ...  It can be useful in facilitating the requirements of those influenced by disaster. The tweets in the actual form involve several slang words and grammatical errors because of tweets informal nature.  ... 
doi:10.14419/ijet.v7i2.21.12394 fatcat:b6ey3aciwnerpopcijvs6wunrq

Multimodal Categorization of Crisis Events in Social Media

Mahdi Abavisani, Liwei Wu, Shengli Hu, Joel Tetreault, Alejandro Jaimes
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the  ...  By processing billions of texts and images a minute, events can be automatically detected to enable emergency response workers to better assess rapidly evolving situations and deploy resources accordingly  ...  and next-sentence prediction tasks as weakly-supervised pre-training.  ... 
doi:10.1109/cvpr42600.2020.01469 dblp:conf/cvpr/AbavisaniWHTJ20 fatcat:ioj4qpl7ynd2rmehadhaqfkdwq


A. Ravi Shankar, J. L. Fernandez-Marquez, B. Pernici, G. Scalia, M. R. Mondardini, G. Serugendo
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders  ...  This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers  ...  Out of the 236 classified tweets, the volunteers fully agreed on 146 classifications (61.35%), reached a high agreement on 57 classifications (23.95%), and did not reach an agreement in 33 classifications  ... 
doi:10.5194/isprs-archives-xlii-3-w8-331-2019 fatcat:5jvgxzzpprdu5mtfjot73ol5ry

Twitter sentiment classification for measuring public health concerns

Xiang Ji, Soon Ae Chun, Zhi Wei, James Geller
2015 Social Network Analysis and Mining  
We are measuring public concern using a two-step sentiment classification approach. In the first step, we distinguish Personal tweets from News (i.e., Non-Personal) tweets.  ...  Based on the number of tweets classified as Personal Negative, we compute a Measure of Concern (MOC) and a timeline of the MOC.  ...  Acknowledgments This work was partially funded by a CUNY PSC research grant and partially sponsored by Intelligent Automation Inc. for the summer internship training for X. Ji.  ... 
doi:10.1007/s13278-015-0253-5 pmid:32226558 pmcid:PMC7096866 fatcat:4uafp234k5bsnbtfeywv5vr42i

Random Forest Location Prediction from Social Networks during Disaster Events

Rachid Ouaret, Babiga Birregah, Eddie Soulier, Samuel Auclair, Faiza Boulahya
2019 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)  
Rapid location and classification of data posted on social networks during time-critical situations such as natural disasters, crowd movement and terrorism is very useful way to gain situational awareness  ...  Identification of Tweets and their precise location are still inaccurate.  ...  This is accomplished through the use of bagging and a classification and regression tree (CART) learning algorithm in order to build a large collection of "de-correlated" decision trees.  ... 
doi:10.1109/snams.2019.8931863 dblp:conf/snams/OuaretBSAB19 fatcat:ykkribqobrguvojqb2dtta72py

Knowledge-Based Tweet Classification for Disease Sentiment Monitoring [chapter]

Xiang Ji, Soon Ae Chun, James Geller
2016 Studies in Computational Intelligence  
Ji et al. weakly correlated with the peaks of the News timeline without any appreciable time delay or lead.  ...  Disease monitoring and tracking is of tremendous value, not only for containing the spread of contagious diseases but also for avoiding unnecessary public concerns and even panic.  ...  supervised training data.  ... 
doi:10.1007/978-3-319-30319-2_17 fatcat:zcbjsn6xpneqvac64lrnhejtf4

Location reference recognition from texts: A survey and comparison [article]

Xuke Hu, Zhiyong Zhou, Hao Li, Yingjie Hu, Fuqiang Gu, Jens Kersten, Hongchao Fan, Friederike Klan
2022 arXiv   pre-print
To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management  ...  , spatial humanities, tourism management, and crime management.  ...  For example, for traffic management and disaster management that rely on informal texts and require fine-grained locations, GazPNE2 is a good option; for crime management and tourism management that rely  ... 
arXiv:2207.01683v1 fatcat:xiy7az4veza6lm52c6yj7mnoe4

Emotion Recognition by Textual Tweets Classification Using Voting Classifier(LR-SGD)

Anam Yousaf, Muhammad Umer, Saima Sadiq, Saleem Ullah, Seyedali Mirjalili, Vaibhav Rupapara, Michele Nappi
2020 IEEE Access  
Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy.  ...  Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies  ...  PROPOSED MODELS FOR TWEETS SENTIMENT CLASSIFICATION In this section classifiers utilized for tweet classification will be discussed.  ... 
doi:10.1109/access.2020.3047831 fatcat:ayncrponabffdopv7oaoofufkm

Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia

Anni Arumsari Fitriany, Piotr J. Flatau, Khoirunurrofik Khoirunurrofik, Nelly Florida Riama
2021 Sustainability  
The policy implications of these results suggest that crowdsourced data can be included in the fire management system in Indonesia to support early fire detection and fire disaster mitigation efforts.  ...  The existing approaches that BMKG and other Indonesian agencies use to detect fire activity are reviewed and a novel approach for early fire detection is proposed based on the crowdsourcing of tweets.  ...  The authors acknowledge the help of Maria Flatau with discussion of large-scale indices. Michał Łabuz processed the TAGGS algorithm and  ... 
doi:10.3390/su132011188 fatcat:sg3a3ayssjbozbjch4nmzzt43i

Localizing and Quantifying Damage in Social Media Images [article]

Xukun Li and Huaiyu Zhang and Doina Caragea and Muhammad Imran
2018 arXiv   pre-print
., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams.  ...  Our proposed approach enables the use of social network images for post-disaster damage assessment and provides an inexpensive and feasible alternative to the more expensive GIS approach.  ...  Many studies have shown the utility of social media information for disaster management and response teams.  ... 
arXiv:1806.07378v1 fatcat:ylojycv4j5fgjjokisjrrrwi6e
« Previous Showing results 1 — 15 out of 163 results