Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1

Capturing stance dynamics in social media: open challenges and research directions

Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Detection of Software Vulnerability Communication in Expert Social Media Channels: A Data-driven Approach

Conceptually, a vulnerability is: A flaw or weakness in a system’s design, implementation,or operation and management that could be exploited to violate the system’s security policy .Some of these flaws can go undetected and exploited for long periods of time after soft-ware release. Although some software providers are making efforts to avoid this situ-ation, inevitability, users are still exposed to vulnerabilities that allow criminal hackersto take advantage. These vulnerabilities are constantly discussed in specialised forumson social media. Therefore, from a cyber security standpoint, the information found inthese places can be used for countermeasures actions against malicious exploitation ofsoftware. However, manual inspection of the vast quantity of shared content in socialmedia is impractical. For this reason, in this thesis, we analyse the real applicability ofsupervised classification models to automatically detect software vulnerability com-munication in expert social media channels. We cover the following three principal aspects: Firstly, we investigate the applicability of classification models in a range of 5 differ-ent datasets collected from 3 Internet Domains: Dark Web, Deep Web and SurfaceWeb. Since supervised models require labelled data, we have provided a systematiclabelling process using multiple annotators to guarantee accurate labels to carry outexperiments. Using these datasets, we have investigated the classification models withdifferent combinations of learning-based algorithms and traditional features represen-tation. Also, by oversampling the positive instances, we have achieved an increaseof 5% in Positive Recall (on average) in these models. On top of that, we have appiiplied Feature Reduction, Feature Extraction and Feature Selection techniques, whichprovided a reduction on the dimensionality of these models without damaging the accuracy, thus, providing computationally efficient models. Furthermore, in addition to traditional features representation, we have investigated the performance of robust language models, such as Word Embedding (WEMB) andSentence Embedding (SEMB) on the accuracy of classification models. RegardingWEMB, our experiment has shown that this model trained with a small security-vocabulary dataset provides comparable results with WEMB trained in a very large general-vocabulary dataset. Regarding SEMB model, our experiment has shown thatits use overcomes WEMB model in detecting vulnerability communication, recording 8% of Avg. Class Accuracy and 74% of Positive Recall. In addition, we investigate twoDeep Learning algorithms as classifiers, text CNN (Convolutional Neural Network)and RNN (Recurrent Neural Network)-based algorithms, which have improved ourmodel, resulting in the best overall performance for our task

Unionization method for changing opinion in sentiment classification using machine learning

Sentiment classification aims to determine whether an opinionated text expresses a positive, negative or neutral opinion. Most existing sentiment classification approaches have focused on supervised text classification techniques. One critical problem of sentiment classification is that a text collection may contain tens or hundreds of thousands of features, i.e. high dimensionality, which can be solved by dimension reduction approach. Nonetheless, although feature selection as a dimension reduction method can reduce feature space to provide a reduced feature subset, the size of the subset commonly requires further reduction. In this research, a novel dimension reduction approach called feature unionization is proposed to construct a more reduced feature subset. This approach works based on the combination of several features to create a more informative single feature. Another challenge of sentiment classification is the handling of concept drift problem in the learning step. Users’ opinions are changed due to evolution of target entities over time. However, the existing sentiment classification approaches do not consider the evolution of users’ opinions. They assume that instances are independent, identically distributed and generated from a stationary distribution, even though they are generated from a stream distribution. In this study, a stream sentiment classification method is proposed to deal with changing opinion and imbalanced data distribution using ensemble learning and instance selection methods. In relation to the concept drift problem, another important issue is the handling of feature drift in the sentiment classification. To handle feature drift, relevant features need to be detected to update classifiers. Since proposed feature unionization method is very effective to construct more relevant features, it is further used to handle feature drift. Thus, a method to deal with concept and feature drifts for stream sentiment classification was proposed. The effectiveness of the feature unionization method was compared with the feature selection method over fourteen publicly available datasets in sentiment classification domain using three typical classifiers. The experimental results showed the proposed approach is more effective than current feature selection approaches. In addition, the experimental results showed the effectiveness of the proposed stream sentiment classification method in comparison to static sentiment classification. The experiments conducted on four datasets, have successfully shown that the proposed algorithm achieved better results and proving the effectiveness of the proposed method

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.Comment: 24 Pages, 8 Tables, 6 Figures; Accepted by PLoS One ; One of the two mentioned Datasets in the manuscript has Closed Access. We will make it public after PLoS One produces the manuscrip

Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions