Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an efficient, reliable and accurate flood text classification model with minimal labeled data. This study is important since it can immensely help in providing the flood-related updates and notifications to the city officials for emergency decision making, rescue operations, and early warnings, etc. We propose to perform the text classification using the inductive transfer learning method i.e pre-trained language model ULMFiT and fine-tune it in order to effectively classify the flood-related feeds in any new location. Finally, we show that using very little new labeled data in the target domain we can successfully build an efficient and high performing model for flood detection and analysis with human-generated facts and observations from Twitter.
Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention
Department of Urban Planning and Design, The University of Hong Kong（香港大学城市规划与设计系）；Urban Systems Institute, The University of Hong Kong（香港大学都市系统研究所）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港理工大学土木与环境工程系）
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance
Columbia University in the City of New York（哥伦比亚大学）；International Research Institute for Climate and Society, Columbia University in the City of New York（哥伦比亚大学国际气候与社会研究所）；Lamont-Doherty Earth Observatory, Columbia University in the City of New York（哥伦比亚大学拉蒙特-多尔蒂地球观测站）；University of Brescia（布雷西亚大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
University of Campinas（坎皮纳斯大学）；Institute of Computing（计算研究所）
Social media has become an important data source for event analysis. When collecting this type of data, most contain no useful information to a target event. Thus, it is essential to filter out those noisy data at the earliest opportunity for a human expert to perform further inspection. Most existing solutions for event filtering rely on fully supervised methods for training. However, in many real-world scenarios, having access to large number of labeled samples is not possible. To deal with a few labeled sample training problem for event filtering, we propose a graph-based few-shot learning …
European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）；Universitat Pompeu Fabra（庞培法布拉大学）；KAJO
This paper describes a prototype system that integrates social media analysis into the European Flood Awareness System (EFAS). This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model. Then, we adopt a multi-lingual approach to find flood-related messages by employing two state-of-the-art methodologies: language-agnostic word embeddings and language-aligned word embeddings. Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled …
University of Arizona（亚利桑那大学）；University of Maryland（马里兰大学）；University of South Florida（南佛罗里达大学）；University of Georgia（佐治亚大学）
Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can cause severe losses to the users, and informing users of their PII exposure is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced automatic techniques are needed. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often …
Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan.（巴基斯坦白沙瓦工程与技术大学计算机系统工程系）；Department of Computer Science, Munsters Technological University, Cork, Ireland.（爱尔兰科克芒斯特理工大学计算机科学系）
In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers bot…
DCSE, University of Engineering and Technology, Peshawar, Pakistan（巴基斯坦白沙瓦工程与技术大学DCSE）；Abasyn University Islamabad Campus, Pakistan（巴基斯坦阿巴辛大学伊斯兰堡校区）；Department of AI, AJOU University, South Korea（韩国亚洲大学人工智能系）；Department of Computer Science, Munster Technological University, Cork, Ireland（爱尔兰科克芒斯特理工大学计算机科学系）
This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectiv…
School of Computer Science（计算机科学学院）；Georgia Institute of Technology（佐治亚理工学院）
The ubiquity of social media makes it a rich source for physical event detection, such as disasters, and as a potential resource for crisis management resource allocation. There have been some recent works on leveraging social media sources for retrospective, after-the-fact event detection of large events such as earthquakes or hurricanes. Similarly, there is a long history of using traditional physical sensors such as climate satellites to perform regional event detection. However, combining social media with corroborative physical sensors for real-time, accurate, and global physical detectio…
Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle to eliminate these contents rapidly. This is because adapting to new domains requires human intensive fact-checking that is slow and difficult to scale. To address this challenge, we propose to leverage news-source credibility labels as weak labels for social media posts and propose model-guided refinement of labels to construct large-scale, diverse misinformation labeled datasets in new domains. The weak…
Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts
Fundamentals of Natural Language Processing, University of Bamberg（自然语言处理基础，巴姆伯格大学）；Department of Sociology, The Ohio State University（社会学系，俄亥俄州立大学）；Institut für Maschinelle Sprachverarbeitung, University of Stuttgart（机械语言处理研究所，斯图加特大学）
Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled data and improve classification performance, we propose to perform these tasks in two steps: 1. Decomposing the task into domain-specific sub-tasks by identifying key concepts, thus utilizing human domain understanding; and 2. Combining the results of learners for each key concept using co-training to reduce the requirements for labeled training data. We empirically show the e…
Prairie View A&M University（普雷里维尤农工大学）；Texas A&M University System（德州农工大学系统）
News in social media such as Twitter has been generated in high volume and speed. However, very few of them can be labeled (as fake or true news) in a short time. In order to achieve timely detection of fake news in social media, a novel deep two-path semi-supervised learning model is proposed, where one path is for supervised learning and the other is for unsupervised learning. These two paths implemented with convolutional neural networks are jointly optimized to enhance detection performance. In addition, we build a shared convolutional neural networks between these two paths to share the l…
University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）
In this paper, we describe our system which participates in the shared task of Hate Speech Detection on Social Networks of VLSP 2019 evaluation campaign. We are provided with the pre-labeled dataset and an unlabeled dataset for social media comments or posts. Our mission is to pre-process and build machine learning models to classify comments/posts. In this report, we use Bidirectional Long Short-Term Memory to build the model that can predict labels for social media text according to Clean, Offensive, Hate. With this system, we achieve comparative results with 71.43% on the public standard te…
Prairie View A&M University（普雷里维尤农工大学）；Texas A&M University System（得克萨斯农工大学系统）
News in social media such as Twitter has been generated in high volume and speed. However, very few of them are labeled (as fake or true news) by professionals in near real time. In order to achieve timely detection of fake news in social media, a novel framework of two-path deep semi-supervised learning is proposed where one path is for supervised learning and the other is for unsupervised learning. The supervised learning path learns on the limited amount of labeled data while the unsupervised learning path is able to learn on a huge amount of unlabeled data. Furthermore, these two paths imp…
This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive labeled examples defines the community. The solution introduces an unsupervised proxy task for learning user embeddings: user re-identification. Experiments with 16 different communities show that the resulting embeddings are more effective for community membership identification than common unsupervised representations.
Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
As the reach of the internet increases, pejorative terms started flooding over social media platforms. This leads to the necessity of identifying hostile content on social media platforms. Identification of hostile contents on low-resource languages like Hindi poses different challenges due to its diverse syntactic structure compared to English. In this paper, we develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts. We formulated this problem as binary classificat…
Qatar Computing Research Institute – HBKU（卡塔尔计算研究所 – 哈马德·本·哈利法大学）
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The Scarcity of labeled data, particularly in the early hours of a crisis, delays the machine learning process. The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for …
SUNY at Buffalo（纽约州立大学布法罗分校）；The University of British Columbia（不列颠哥伦比亚大学）
Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our…
Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related…
Islamic University of Technology（伊斯兰技术大学）；York University（约克大学）；University of Rajshahi（拉杰沙希大学）
Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annota…
Vicomtech Foundation（维康泰克基金会）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA））；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））
Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partiall…
Auckland University of Technology（奥克兰理工大学）；Deakin University（迪肯大学）；University of Tasmania（塔斯马尼亚大学）
Public concern detection provides potential guidance to the authorities for crisis management before or during a pandemic outbreak. Detecting people's concerns and attention from online social media platforms has been widely acknowledged as an effective approach to relieve public panic and prevent a social crisis. However, detecting concerns in time from massive information in social media turns out to be a big challenge, especially when sufficient manually labeled data is in the absence of public health emergencies, e.g., COVID-19. In this paper, we propose a novel end-to-end deep learning mo…
University of Halabja（哈拉卜贾大学）；University of Kurdistan-Hawlêr（库尔德斯坦-霍勒大学）
The Facebook application is used as a resource for collecting the comments of this dataset, The dataset consists of 6756 comments to create a Medical Kurdish Dataset (MKD). The samples are comments of users, which are gathered from different posts of pages (Medical, News, Economy, Education, and Sport). Six steps as a preprocessing technique are performed on the raw dataset to clean and remove noise in the comments by replacing characters. The comments (short text) are labeled for positive class (medical comment) and negative class (non-medical comment) as text classification. The percentage r…
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
The rapid development of social media changes the lifestyle of people and simultaneously provides an ideal place for publishing and disseminating rumors, which severely exacerbates social panic and triggers a crisis of social trust. Early content-based methods focused on finding clues from the text and user profiles for rumor detection. Recent studies combine the stances of users' comments with news content to capture the difference between true and false rumors. Although the user's stance is effective for rumor detection, the manual labeling process is time-consuming and labor-intensive, whic…
University of Information Technology, Vietnam National University Ho Chi Minh City（胡志明市国家大学信息技术大学）
In recent years, Hate Speech Detection has become one of the interesting fields in natural language processing or computational linguistics. In this paper, we present the description of our system to solve this problem at the VLSP shared task 2019: Hate Speech Detection on Social Networks with the corpus which contains 20,345 human-labeled comments/posts for training and 5,086 for public-testing. We implement a deep learning method based on the Bi-GRU-LSTM-CNN classifier into this task. Our result in this task is 70.576% of F1-score, ranking the 5th of performance on public-test set.
Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from …
University of Massachusetts Boston（马萨诸塞大学波士顿分校）；Disney Research（迪士尼研究院）
An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvot…
Social media is daily creating massive multimedia content with paired image and text, presenting the pressing need to automate the vision and language understanding for various multimodal classification tasks. Compared to the commonly researched visual-lingual data, social media posts tend to exhibit more implicit image-text relations. To better glue the cross-modal semantics therein, we capture hinting features from user comments, which are retrieved via jointly leveraging visual and lingual similarity. Afterwards, the classification tasks are explored via self-training in a teacher-student f…
The framing of political issues can influence policy and public opinion. Even though the public plays a key role in creating and spreading frames, little is known about how ordinary people on social media frame political issues. By creating a new dataset of immigration-related tweets labeled for multiple framing typologies from political communication theory, we develop supervised models to detect frames. We demonstrate how users' ideology and region impact framing choices, and how a message's framing influences audience responses. We find that the more commonly-used issue-generic frames obscu…
