Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies

2020 ◽  
pp. 33-50
Author(s):  
Manzhu Yu ◽  
Qunying Huang ◽  
Han Qin ◽  
Chris Scheele ◽  
Chaowei Yang
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Case Studies ◽  
Text Classification ◽  
Situation Awareness ◽  
Social Media Text

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

scholarly journals Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

2020 ◽  
Vol 33 (11) ◽  
pp. 2169-2185 ◽  
Author(s):  
Andrew J. Schaumberg ◽  
Wendy C. Juarez-Nicanor ◽  
Sarah J. Choudhury ◽  
Laura G. Pastrián ◽  
Bobbi S. Pritt ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Patient Care ◽  
Real Time ◽  
Improve Patient Care ◽  
Low Grade ◽  
Search Performance ◽  
Cell Nuclei ◽  
Disease States ◽  
Share Data

Abstract Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805–0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.

scholarly journals To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

2019 ◽  
Vol 8 (12) ◽  
pp. 4433-4435
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Social Media Text ◽  
The Impact

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

scholarly journals Comparison of pretraining models and strategies for health-related social media text classification

2021 ◽  
Author(s):  
Yuting Guo ◽  
Yao Ge ◽  
Yuan-Chi Yang ◽  
Mohammed Ali Al-Garadi ◽  
Abeed Sarker
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Text Classification ◽  
High Performance ◽  
Language Models ◽  
Learning Performance ◽  
Health Related ◽  
Social Media Text ◽  
Performance Results ◽  
Better Than

Motivation Pretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks. There is a need to benchmark such models for targeted NLP tasks, and to explore effective pretraining strategies to improve machine learning performance. Results In this work, we addressed the task of health-related social media text classification. We benchmarked five models-RoBERTa, BERTweet, TwitterBERT, BioClinical_BERT, and BioBERT on 22 tasks. We attempted to boost performance for the best models by comparing distinct pretraining strategies-domain-adaptive pretraining (DAPT), source-adaptive pretraining (SAPT), and topic-specific pretraining (TSPT). RoBERTa and BERTweet performed comparably in most tasks, and better than others. For pretraining strategies, SAPT performed better or comparable to the off-the-shelf models, and significantly outperformed DAPT. SAPT+TSPT showed consistently high performance, with statistically significant improvement in one task. Our findings demonstrate that RoBERTa and BERTweet are excellent off-the-shelf models for health-related social media text classification, and extended pretraining using SAPT and TSPT can further improve performance.

scholarly journals Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models

2018 ◽  
Vol 2672 (42) ◽  
pp. 247-256
Author(s):  
Tao Wen ◽  
Adriana-Simona Mihăiţă ◽  
Hoang Nguyen ◽  
Chen Cai ◽  
Fang Chen
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Case Studies ◽  
Situation Awareness ◽  
Large Scale ◽  
Traffic Simulation ◽  
Data Driven ◽  
Incident Management ◽  
Management Platform ◽  
Transport Management

This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.

scholarly journals Monitoring global trends in Covid-19 vaccination intention and confidence: a social media-based deep learning study

2021 ◽  
Author(s):  
Xinyu Zhou ◽  
Alex de Figueiredo ◽  
Qin Xu ◽  
Leesa Lin ◽  
Per E Kummervold ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Eastern Mediterranean ◽  
Learning Models ◽  
Social Media Data ◽  
Emerging Trends ◽  
The Us ◽  
Media Monitoring ◽  
Media Data

AbstractBackgroundThis study developed deep learning models to monitor global intention and confidence of Covid-19 vaccination in real time.MethodsWe collected 6.73 million English tweets regarding Covid-19 vaccination globally from January 2020 to February 2021. Fine-tuned Transformer-based deep learning models were used to classify tweets in real time as they relate to Covid-19 vaccination intention and confidence. Temporal and spatial trends were performed to map the global prevalence of Covid-19 vaccination intention and confidence, and public engagement on social media was analyzed.FindingsGlobally, the proportion of tweets indicating intent to accept Covid-19 vaccination declined from 64.49% on March to 39.54% on September 2020, and then began to recover, reaching 52.56% in early 2021. This recovery in vaccine acceptance was largely driven by the US and European region, whereas other regions experienced the declining trends in 2020. Intent to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia, Eastern Mediterranean, and Western Pacific regions, but low in American, European, and African regions. 12.71% tweets expressed misinformation or rumors in South Korea, 14.04% expressed distrust in government in the US, and 16.16% expressed Covid-19 vaccine being unsafe in Greece, ranking first globally. Negative tweets, especially misinformation or rumors, were more engaged by twitters with fewer followers than positive tweets.InterpretationThis global real-time surveillance study highlights the importance of deep learning based social media monitoring to detect emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions.FundingNational Natural Science Foundation of China.Research in contextEvidence before this studyWith COVID-19 vaccine rollout, each country should investigate its vaccination intention in local contexts to ensure massive vaccination. We searched PubMed for all articles/preprints until April 9, 2021 with the keywords “(“Covid-19 vaccines”[Mesh] OR Covid-19 vaccin*[TI]) AND (confidence[TI] OR hesitancy[TI] OR acceptance[TI] OR intention[TI])”. We identified more than 100 studies, most of which are country-level cross-sectional surveys, and the largest global survey of Covid-19 vaccine acceptance only covered 32 countries to date. However, how Covid-19 vaccination intention changes over time remain unknown, and many countries are not covered in previous surveys yet. A few studies assessed public sentiments towards Covid-19 vaccination using social media data, but only targeting limited geographical areas. There is a lack of real-time surveillance, and no study to date has globally monitored Covid-19 vaccination intention in real time.Added value of this studyTo our knowledge, this is the largest global monitoring study of Covid-19 vaccination intention and confidence with social media data in over 100 countries from the beginning of the pandemic to February 2021. This study developed deep learning models by fine-tuning a Bidirectional Encoder Representation from Transformer (BERT)-based model with 8000 manually-classified tweets, which can be used to monitor Covid-19 vaccination beliefs using social media data in real time. It achieves temporal and spatial analyses of the evolving beliefs to Covid-19 vaccines across the world, and also an insight for many countries not yet covered in previous surveys. This study highlights that the intention to accept Covid-19 vaccination have experienced a declining trend since the beginning of the pandemic in all world regions, with some regions recovering recently, though not to their original levels. This recovery was largely driven by the US and European region (EUR), whereas other regions experienced the declining trends in 2020. Intention to accept and confidence of Covid-19 vaccination were relatively high in South-East Asia region (SEAR), Eastern Mediterranean region (EMR), and Western Pacific region (WPR), but low in American region (AMR), EUR, and African region (AFR). Many AFR countries worried more about vaccine effectiveness, while EUR, AMR, and WPR concerned more about vaccine safety (the most concerns with 16.16% in Greece). Online misinformation or rumors were widespread in AMR, EUR, and South Korea (12.71%, ranks first globally), and distrust in government was more prevalent in AMR (14.04% in the US, ranks first globally). Our findings can be used as a reference point for survey data on a single country in the future, and inform timely and specific interventions for each country to address Covid-19 vaccine hesitancy.Implications of all the available evidenceThis global real-time surveillance study highlights the importance of deep learning based social media monitoring as a quick and effective method for detecting emerging trends of Covid-19 vaccination intention and confidence to inform timely interventions, especially in settings with limited sources and urgent timelines. Future research should build multilingual deep learning models and monitor Covid-19 vaccination intention and confidence in real time with data from multiple social media platforms.

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