Automatic identification of eyewitness messages on twitter during disasters

Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other potential uses. However, the identification of eyewitness reports on Twitter is a challenging task. This work investigates different types of sources on tweets related to eyewitnesses and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitnesses, and (iii) vulnerable eyewitnesses. Moreover, we investigate various characteristics associated with each kind of eyewitness type. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We use these characteristics and labeled data to train several machine learning classifiers. Our results performed on several real-world Twitter datasets reveal that textual features (bag-of-words) when combined with domain-expert features achieve better classification performance. Our approach contributes a successful example for combining crowdsourced and machine learning analysis, and increases our understanding and capability of identifying valuable eyewitness reports during disasters.     

Search task success evaluation by exploiting multi-view active semi-supervised learning

Search task success rate is an important indicator to measure the performance of search engines. In contrast to most of the previous approaches that rely on labeled search tasks provided by users or third-party editors, this paper attempts to improve the performance of search task success evaluation by exploiting unlabeled search tasks that are existing in search logs as well as a small amount of labeled ones. Concretely, the Multi-view Active Semi-Supervised Search task Success Evaluation (MA4SE) approach is proposed, which exploits labeled data and unlabeled data by integrating the advantages of both semi-supervised learning and active learning with the multi-view mechanism. In the semi-supervised learning part of MA4SE, we employ a multi-view semi-supervised learning approach that utilizes different parameter configurations to achieve the disagreement between base classifiers. The base classifiers are trained separately from the pre-defined action and time views. In the active learning part of MA4SE, each classifier received from semi-supervised learning is applied to unlabeled search tasks, and the search tasks that need to be manually annotated are selected based on both the degree of disagreement between base classifiers and a regional density measurement. We evaluate the proposed approach on open datasets with two different definitions of search tasks success. The experimental results show that MA4SE outperforms the state-of-the-art semi-supervised search task success evaluation approach.     

A Natural Language Processing Approach to Understanding Context in the Extraction and GeoCoding of Historical Floods,Storms, and Adaptation Measures

Despite the known financial, economical, and humanitarian impacts of hurricanes and the floods that follow, datasets consisting of flood and flood risk reduction projects are either small in scope, lack in details, or held privately by commercial holders. However, with the amount of online data growing exponentially, we have seen a rise of information extraction techniques on unstructured text to drive insights. On one hand, social media in particular has seen a tremendous increase in popularity. On the other hand, despite this popularity, social media has proven to be unreliable and difficult to extract full information from. In contrast, online newspapers are often vetted by a journalist, and consist of more fine details. As a result, in this paper we leverage Natural Language Processing (NLP) to create a hybrid Named-Entity Recognition (NER) model that employs a domain-specific machine learning model, linguistic features, and rule-based matching to extract information from newspapers. To the knowledge of the authors, this model is the first of its kind to extract detailed flooding information and risk reduction projects over the entire contiguous United States. The approach used in this paper expands upon previous similar works by widening the geographical location and applying techniques to extract information over large documents, with minimal accuracy loss from the previous methods. Specifically, our model is able to extract information such as street closures, project costs, and metrics. Our validation indicates an F1 score of 72.13% for the NER model entity extraction, a binary classification location filter with a score of 73%, and an overall performance only 8.4% lower than a human validator against a gold-standard. Through this process, we find the location of 27,444 streets, 181,076 flood risk reduction projects, and 435,353 storm locations throughout the United States in the past two decades.     

MMED: A multi-domain and Multi-modality event dataset

In this work, we release a multi-domain and multi-modality event dataset (MMED), containing 25,052 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, BBC News, etc.) and 75,884 image posts shared on Flickr by thousands of social media users. The articles contributed by professional journalists and the images shared by amateur users are annotated according to 410 real-world events, covering emergencies, natural disasters, sports, ceremonies, elections, protests, military intervention, economic crises, etc. The MMED dataset is collected by the following the principles of high relevance in supporting the application needs, a wide range of event types, non-ambiguity of the event labels, imbalanced event clusters, and difficulty discriminating the event labels. The dataset can stimulate innovative research on related challenging problems, such as (weakly aligned) cross-modal retrieval and cross-domain event discovery, inspire visual relation mining and reasoning, etc. For comparisons, 15 baselines for two scenarios have been quantitatively and qualitatively evaluated using the dataset.     

Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics

Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions.In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.     

EGC: A novel event-oriented graph clustering framework for social media text

With the popularity of social platforms such as Sina Weibo, Tweet, etc., a large number of public events spread rapidly on social networks and huge amount of textual data are generated along with the discussion of netizens. Social text clustering has become one of the most critical methods to help people find relevant information and provides quality data for subsequent timely public opinion analysis. Most existing neural clustering methods rely on manual labeling of training sets and take a long time in the learning process. Due to the explosiveness and the large-scale of social media data, it is a challenge for social text data clustering to satisfy the timeliness demand of users. This paper proposes a novel unsupervised event-oriented graph clustering framework (EGC), which can achieve efficient clustering performance on large-scale datasets with less time overhead and does not require any labeled data. Specifically, EGC first mines the potential relations existing in social text data and transforms the textual data of social media into an event-oriented graph by taking advantage of graph structure for complex relations representation. Secondly, EGC uses a keyword-based local importance method to accurately measure the weights of relations in event-oriented graph. Finally, a bidirectional depth-first clustering algorithm based on the interrelations is proposed to cluster the nodes in event-oriented graph. By projecting the relations of the graph into a smaller domain, EGC achieves fast convergence. The experimental results show that the clustering performance of EGC on the Weibo dataset reaches 0.926 (NMI), 0.926 (AMI), 0.866 (ARI), which are 13%–30% higher than other clustering methods. In addition, the average query time of EGC clustered data is 16.7ms, which is 90% less than the original data.     

Fuzzy topic modeling approach for text mining over short text

In this era, the proliferating role of social media in our lives has popularized the posting of the short text. The short texts contain limited context with unique characteristics which makes them difficult to handle. Every day billions of short texts are produced in the form of tags, keywords, tweets, phone messages, messenger conversations social network posts, etc. The analysis of these short texts is imperative in the field of text mining and content analysis. The extraction of precise topics from large-scale short text documents is a critical and challenging task. The conventional approaches fail to obtain word co-occurrence patterns in topics due to the sparsity problem in short texts, such as text over the web, social media like Twitter, and news headlines. Therefore, in this paper, the sparsity problem is ameliorated by presenting a novel fuzzy topic modeling (FTM) approach for short text through fuzzy perspective. In this research, the local and global term frequencies are computed through a bag-of-words (BOW) model. To remove the negative impact of high dimensionality on the global term weighting, the principal component analysis is adopted; thereafter the fuzzy c-means algorithm is employed to retrieve the semantically relevant topics from the documents. The experiments are conducted over the three real-world short text datasets: the snippets dataset is in the category of small dataset whereas the other two datasets, Twitter and questions, are the bigger datasets. Experimental results show that the proposed approach discovered the topics more precisely and performed better as compared to other state-of-the-art baseline topic models such as GLTM, CSTM, LTM, LDA, Mix-gram, BTM, SATM, and DREx+LDA. The performance of FTM is also demonstrated in classification, clustering, topic coherence and execution time. FTM classification accuracy is 0.95, 0.94, 0.91, 0.89 and 0.87 on snippets dataset with 50, 75, 100, 125 and 200 number of topics. The classification accuracy of FTM on questions dataset is 0.73, 0.74, 0.70, 0.68 and 0.78 with 50, 75, 100, 125 and 200 number of topics. The classification accuracies of FTM on snippets and questions datasets are higher than state-of-the-art baseline topic models.     

FALCoN: Detecting and classifying abusive language in social networks using context features and unlabeled data

Social networks have grown into a widespread form of communication that allows a large number of users to participate in conversations and consume information at any time. The casual nature of social media allows for nonstandard terminology, some of which may be considered rude and derogatory. As a result, a significant portion of social media users is found to express disrespectful language. This problem may intensify in certain developing countries where young children are granted unsupervised access to social media platforms. Furthermore, the sheer amount of social media data generated daily by millions of users makes it impractical for humans to monitor and regulate inappropriate content. If adolescents are exposed to these harmful language patterns without adequate supervision, they may feel obliged to adopt them. In addition, unrestricted aggression in online forums may result in cyberbullying and other dreadful occurrences. While computational linguistics research has addressed the difficulty of detecting abusive dialogues, issues remain unanswered for low-resource languages with little annotated data, leading the majority of supervised techniques to perform poorly. In addition, social media content is often presented in complex, context-rich formats that encourage creative user involvement. Therefore, we propose to improve the performance of abusive language detection and classification in a low-resource setting, using both the abundant unlabeled data and the context features via the co-training protocol that enables two machine learning models, each learning from an orthogonal set of features, to teach each other, resulting in an overall performance improvement. Empirical results reveal that our proposed framework achieves F1 values of 0.922 and 0.827, surpassing the state-of-the-art baselines by 3.32% and 45.85% for binary and fine-grained classification tasks, respectively. In addition to proving the efficacy of co-training in a low-resource situation for abusive language detection and classification tasks, the findings shed light on several opportunities to use unlabeled data and contextual characteristics of social networks in a variety of social computing applications.     

A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media

Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions.     

钢板表面缺陷深度主动学习高效分类方法

针对钢板表面缺陷图像分类传统深度学习算法中需要大量标签数据的问题,提出一种基于主动学习的高效分类方法。该方法包含一个轻量级的卷积神经网络和一个基于不确定性的主动学习样本筛选策略。神经网络采用简化的convolutional base进行特征提取,然后用全局池化层替换掉传统密集连接分类器中的隐藏层来减轻过拟合。为了更好的衡量模型对未标签图像样本所属类别的不确定性,首先将未标签图像样本传入到用标签图像样本训练好的模型,得到模型对每一个未标签样本关于标签的概率分布(probability distribution over classes, PDC),然后用此模型对标签样本进行预测并得到模型对每个标签的平均PDC。将两类分布的KL-divergence值作为不确定性指标来筛选未标签图像进行人工标注。根据在NEU-CLS开源缺陷数据集上的对比实验,该方法可以通过44%的标签数据实现97%的准确率,极大降低标注成本。     

Semi-supervised document retrieval

This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSRank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSRank can effectively leverage the use of unlabeled data in learning.     

A framework for pre-processing of social media feeds based on integrated local knowledge base

Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings.     

ISE-Hate: A benchmark corpus for inter-faith,sectarian, and ethnic hatred detection on social media in Urdu

Social media has become the most popular platform for free speech. This freedom of speech has given opportunities to the oppressed to raise their voice against injustices, but on the other hand, this has led to a disturbing trend of spreading hateful content of various kinds. Pakistan has been dealing with the issue of sectarian and ethnic violence for the last three decades and now due to freedom of speech, there is a growing trend of disturbing content about religion, sect, and ethnicity on social media. This necessitates the need for an automated system for the detection of controversial content on social media in Urdu which is the national language of Pakistan. The biggest hurdle that has thwarted the Urdu language processing is the scarcity of language resources, annotated datasets, and pretrained language models. In this study, we have addressed the problem of detecting Interfaith, Sectarian, and Ethnic hatred on social media in Urdu language using machine learning and deep learning techniques. In particular, we have: (1) developed and presented guidelines for annotating Urdu text with appropriate labels for two levels of classification, (2) developed a large dataset of 21,759 tweets using the developed guidelines and made it publicly available, and (3) conducted experiments to compare the performance of eight supervised machine learning and deep learning techniques, for the automated identification of hateful content. In the first step, experiments are performed for the hateful content detection as a binary classification task, and in the second step, the classification of Interfaith, Sectarian and Ethnic hatred detection is performed as a multiclass classification task. Overall, Bidirectional Encoder Representation from Transformers (BERT) proved to be the most effective technique for hateful content identification in Urdu tweets.     

Emotional Text Mining: Customer profiling in brand management

The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, highlighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments.     

AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world. These dialects differ according to the country and region of the speaker, and their textual content is constantly growing with the rise of social media networks and web blogs. Although research on Natural Language Processing (NLP) on standard Arabic, namely Modern Standard Arabic (MSA), has witnessed remarkable progress, research efforts on DA are rather limited. This is due to numerous challenges, such as the scarcity of labeled data as well as the nature and structure of DA. While some recent works have reached decent results on several DA sentence classification tasks, other complex tasks, such as sequence labeling, still suffer from weak performances when it comes to DA varieties with either a limited amount of labeled data or unlabeled data only. Besides, it has been shown that zero-shot transfer learning from models trained on MSA does not perform well on DA. In this paper, we introduce AdaSL, a new unsupervised domain adaptation framework for Arabic multi-dialectal sequence labeling, leveraging unlabeled DA data, labeled MSA data, and existing multilingual and Arabic Pre-trained Language Models (PLMs). The proposed framework relies on four key components: (1) domain adaptive fine-tuning of multilingual/MSA language models on unlabeled DA data, (2) sub-word embedding pooling, (3) iterative self-training on unlabeled DA data, and (4) iterative DA and MSA distribution alignment. We evaluate our framework on multi-dialectal Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks.The overall results show that the zero-shot transfer learning, using our proposed framework, boosts the performance of the multilingual PLMs by 40.87% in macro-F1 score for the NER task, while it boosts the accuracy by 6.95% for the POS tagging task. For the Arabic PLMs, our proposed framework increases performance by 16.18% macro-F1 for the NER task and 2.22% accuracy for the POS tagging task, and thus, achieving new state-of-the-art zero-shot transfer learning performance for Arabic multi-dialectal sequence labeling.     

Social media overload,exhaustion, and use discontinuance: Examining the effects of information overload,system feature overload,and social overload

While users’ discontinuance of use has posed a challenge for social media in recent years, there is a paucity of knowledge on the relationships between different dimensions of overload and how overload adversely affects users’ social media discontinuance behaviors. To address this knowledge gap, this study employed the stressor–strain–outcome (SSO) framework to explain social media discontinuance behaviors from an overload perspective. It also conceptualized social media overload as a multidimensional construct consisting of system feature overload, information overload, and social overload. The proposed research model was empirically validated via 412 valid questionnaire responses collected from Facebook users. Our results indicated that the three types of overload are interconnected through system feature overload. System feature overload, information overload, and social overload engender user exhaustion, which in turn leads to users’ discontinued usage of social media. This study extends current technostress research by demonstrating the value of the SSO perspective in explaining users’ social media discontinuance.     

Supervised sentiment analysis in Czech social media

This article describes in-depth research on machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in the case of the Czech language no systematic research has yet been conducted. We tackle this issue and establish a common ground for further research by providing a large human-annotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance. Moreover, in addition to our newly created social media dataset, we also report results for other popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition, potentially leading to the production of high-end commercial solutions.     

Social media platforms and social enterprise: Bibliometric analysis and systematic review

A considerable number of contributions at the intersection of social media platforms and social entrepreneurship has been witnessed over the past decade. The multi-disciplinary nature of current literature necessitates integrative insights on the topic. We thus undertake a two-pronged approach – bibliometric analysis followed by systematic review – to map the extant literature in a structured, objective, and quantitative way. We contribute to the literature as we (i) offer a classification of the literature in three clusters, namely ‘social media platforms, social collaboration and social marketing’, ‘social media platforms and crowdfunding, and ‘social media platforms and crowdsourcing’; (ii) analyze recent research development in each cluster, methodological development, countries co-authorship and evolution of research in the area; and (iii) propose a conceptual framework -accompanied by research propositions- that provides an overview of the literature at the intersection of social media platforms, various social entrepreneurial practices and their influence on the social enterprise performance. Our review culminates with numerous impactful research avenues for scholarly progression in the area. From a practical standpoint, this review integrates scattered findings into one body, allowing the practitioners and policymakers to discern the role of social media platforms in dealing with emerging societal problems and increasing operational efficiencies of social enterprises (SEs). To the best of our knowledge, this is the first review of its kind, offering unique perspectives at the nexus of social media platforms and SE’s performance.