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The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. [ABSTRACT FROM AUTHOR]

Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks. DL has emerged as a promising ML technique that adapts multiple layers of representations or features of the data and yields state-of-the-art extrapolation results. In recent years, along with the success of ML and DL in many other application domains, both ML and DL are also popularly used in SM analysis. This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis. Finally, this review serves the purpose of offering suggestions, ideas, and proposals, along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed. [ABSTRACT FROM AUTHOR]

The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial preprocessing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively. [ABSTRACT FROM AUTHOR]

Social media has given voice to people around the globe. However, all voices are not counted due to the scarcity of lexical computational resources. Such resources could harness the torrent of social media text data. Computational resources for rich languages such as English are available. More are being developed, meanwhile strengthening and enhancing the current ones. However, Roman Sindhi, a resource-poor writing style, is a phonetically rich language lacking computational resources, creating a working space for researchers. This work attempts to develop lexical sentiment resources that will help calculate the public opinion expressed in Roman Sindhi and bring their point of view into the limelight. This work is one of the initial efforts to develop lexical Roman Sindhi sentiment dictionary resources to help detect sentiment orientation in a text. Furthermore, it also developed two interfaces to leverage the lexical resources--a Roman Sindhi to English translator (RoSET) that translates a Roman Sindhi feature into an equivalent English word and a Roman Sindhi rule-based sentiment scorer (RBRS3) that assigns sentiment score to a Roman Sindhi script features. The results obtained from the developed system accommodated the bilingual dataset (Roman Sindhi + English) more adequately. An increase of 20.8% was recorded for positive sentence detection, and a 16% increase was obtained for negative sentences, whereas neutral sentences were marginalized to a lower number (59.31% decrease). The resultant system makes those public voices expressed in the Roman Sindhi script get counted, which otherwise are in vain. [ABSTRACT FROM AUTHOR]

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs. Exploring individuals’ sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public. An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning (VADER). In this study applied twitter intelligence tool (TWINT), Natural Language Toolkit (NLTK), and VADER constitute the three main tools. VADER represents a gold-standard sentiment lexicon, which is basically tailored to attitudes that are communicated by using social media. The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier called VADER to analyze the sentiment of the general population, particularly among thalassemia carriers on the social media platform Twitter. In this study, the results showed that the proposed approach achieved 0.829, 0.816, and 0.818 regarding precision, recall, together with F-score, respectively. The tweets were crawled using the search keywords, “thalassemia screening,” thalassemia test, “and thalassemia diagnosis”. Finally, results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets, respectively. [ABSTRACT FROM AUTHOR]

Social media platforms have become a substratum for people to enunciate their opinions and ideas across the globe. Due to anonymity preservation and freedom of expression, it is possible to humiliate individuals and groups, disregarding social etiquette online, inevitably proliferating and diversifying the incidents of cyberbullying and cyber hate speech. This intimidating problem has recently sought the attention of researchers and scholars worldwide. Still, the current practices to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the recent prevalence of regional languages in social media, the dearth of language resources, and flexible detection approaches, specifically for low-resource languages. In this context, most existing studies are oriented towards traditional resource-rich languages and highlight a huge gap in recently embraced resource-poor languages. One such language currently adopted worldwide and more typically by South Asian users for textual communication on social networks is Roman Urdu. It is derived from Urdu and written using a Left-to-Right pattern and Roman scripting. This language elicits numerous computational challenges while performing natural language preprocessing tasks due to its inflections, derivations, lexical variations, and morphological richness. To alleviate this problem, this research proposes a cyberbullying detection approach for analyzing textual data in the Roman Urdu language based on advanced preprocessing methods, voting-based ensemble techniques, and machine learning algorithms. The study has extracted a vast number of features, including statistical features, word N-Grams, combined n-grams, and BOW model with TFIDF weighting in different experimental settings using GridSearchCV and cross-validation techniques. The detection approach has been designed to tackle users' textual input by considering user-specific writing styles on social media in a colloquial and non-standard form. The experimental results show that SVM with embedded hybrid N-gram features produced the highest average accuracy of around 83%. Among the ensemble voting-based techniques, XGboost achieved the optimal accuracy of 79%. Both implicit and explicit Roman Urdu instances were evaluated, and the categorization of severity based on prediction probabilities was performed. Time complexity is also analyzed in terms of execution time, indicating that LR, using different parameters and feature combinations, is the fastest algorithm. The results are promising with respect to standard assessment metrics and indicate the feasibility of the proposed approach in cyberbullying detection for the Roman Urdu language. [ABSTRACT FROM AUTHOR]

Over the past few decades, multiple software development process models, tools, and techniques have been used by practitioners. Despite using these techniques, most software development organizations still fail to meet customer’s needs within time and budget. Time overrun is one of the major reasons for project failure. There is a need to come up with a comprehensive solution that would increase the chances of project success. However, the “make vs. buy” decision can be helpful for “in time” software development. Social media have become a popular platform for discussion of all sorts of topics, so software development is no exception. Software developers discuss all the pros and cons of making vs. buy decisions on Twitter and other social media platforms. Twitter trending is a typical feature that evaluates the level of popularity of a specific event on online networking. A mixed-method approach comprising of interviews of software industry experts and Twitter data extraction is applied to scrutinize the effective decision of software build vs. buy decision. The findings of the analysis show that software makes vs. buy decisions depend on several factors including cost, development technology, software development team skills, and time. Based on the finding of the study a framework is proposed for the decision to build versus buy in Small and medium-sized enterprises (SMEs). Furthermore, the framework has been designed to statistically indicate make versus buy decisions of the organization and to suggest appropriate choices based on different parameters. [ABSTRACT FROM AUTHOR]

In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%. [ABSTRACT FROM AUTHOR]