Relevancy assessment of tweets using supervised learning techniques: Mining emergency related tweets for automated relevancy classification

Habdank, Matthias; Rodehutskors, Nikolai; Koch, Rainer

doi:10.1109/ict-dm.2017.8275670

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…To classify a comment as relevant or not relevant, a text classification model was built based on the previous definition. The conventional method of building a text classification model 19 , 20 was followed, in which the text is first converted to vectors using standard vectorization techniques (Count Vectorizer and term frequency–inverse document frequency [TF-IDF]) and also with combinations of vectorization techniques: Count Vectorizer and TF-IDF, Count Vectorizer and Doc2Vec, 21 TF-IDF and Doc2Vec, and long short-term memory networks and convolutional neural networks. Second, these vectorization techniques were incorporated into the following models: support vector machines, 22–24 naive Bayes, recurrent neural network, stochastic gradient descent classifier, random forest, 22–24 and XGBoost.…”

Section: Methodsmentioning

confidence: 99%

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Dreyfus

Chaudhary²,

Bhardwaj³

et al. 2021

Journal of the American Medical Informatics Association

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Objective Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts. Materials and Methods Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug. Results Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%. Conclusions The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.

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Section: Methodsmentioning

confidence: 99%

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Dreyfus

Chaudhary²,

Bhardwaj³

et al. 2021

Journal of the American Medical Informatics Association

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“…Imran et al [25] presented a methodology exploiting Tweets content to gain situational awareness while Yin et al [26] attempted to discover important topics in tweets using a burst-detection module. More recently, Habdank et al [27], focused on analysing the content of tweets and categorising them according to their emergency types. For example, for an emergency situation on critical infrastructures they would inspect if a tweet contains words related to infrastructures (e.g., roads).…”

Section: A Social Sensing For Emergency Responsementioning

confidence: 99%

Towards a Social-media Driven Multi-Drone Tasking platform

Terzi

Kolios

Panayiotou

et al. 2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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We present a framework for utilising social-media for tasking drone swarms in search and rescue missions. As elaborated in this work, social-media are a source of timely and valuable information regarding the occurrence, the evolution and the damage produced in disasters. Exploiting this source of knowledge can be very valuable in planning search and rescue missions and identifying tasks to be conducted. In this work a novel multi-drone search and rescue framework is developed that considers social-media signals in the creation of search tasks. The key design challenges of implementing the proposed framework are discussed and a simulated scenario based on a real-life Twitter data set is presented as a means of providing evidence of the effectiveness of the proposed framework.

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Section: Classificationmentioning

confidence: 99%

“…The practice of obtaining tweets for analysis that satisfy specific words is proposed by [22] for disaster detection. More specifically, [22] combine manual oversight with a binary text classifier to establish if an emergency-related tweet is 'relevant' or 'irrelevant'. To develop the tool, 3,785 normalised tweets are used that relate to an explosion at a power plant in Ludwigshafen, Germany in 2016.…”

Section: Classificationmentioning

confidence: 99%

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

Loynes¹,

Ouenniche

Smedt

2020

Ann Oper Res

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This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a Density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster's geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an onthe-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further.

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Relevancy assessment of tweets using supervised learning techniques: Mining emergency related tweets for automated relevancy classification

Cited by 16 publications

References 10 publications

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Towards a Social-media Driven Multi-Drone Tasking platform

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

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