Readings in information retrieval.

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Deep learning for misinformation detection on online social networks: a survey and new perspectives

One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.

https://www.mdpi.com/1660-4601/17/7/2430/pdf

A New Application of Social Impact in Social Media for Overcoming Fake News in Health.

Researchers have collected Twitter data to study a wide range of topics. This growing body of literature, however, has not yet been reviewed systematically to synthesize Twitter-related papers. The existing literature review papers have been limited by constraints of traditional methods to manually select and analyze samples of topically related papers. The goals of this retrospective study are to identify dominant topics of Twitter-based research, summarize the temporal trend of topics, and interpret the evolution of topics withing the last ten years. This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach. This study collected relevant papers from three databases and applied text mining and trend analysis to detect semantic patterns and explore the yearly development of research themes across a decade. We found 38 topics in more than 18,000 manuscripts published between 2006 and 2019. By quantifying temporal trends, this study found that while 23.7% of topics did not show a significant trend (P = 0.05), 21% of topics had increasing trends and 55.3% of topics had decreasing trends that these hot and cold topics represent three categories: application, methodology, and technology. The contributions of this paper can be utilized in the growing field of Twitter-based research and are beneficial to researchers, educators, and publishers.

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Twitter and Research: A Systematic Literature Review Through Text Mining

Social media is used as a dominant source of news distribution among users. The world's preeminent decisions such as politics are acclaimed by social media to influence users for enclosing users' decisions in their favor. However, the adoption of social media is much needed for awareness but the authenticity of content is an unknown factor in the current scenario. Therefore, this research work finds it imperative to propose a solution to fake news detection and classification. In the case of fake news, content is the prime entity that captures the human mind towards trust for specific news. Therefore, a linguistic model is proposed to find out the properties of content that will generate language-driven features. This linguistic model extracts syntactic, grammatical, sentimental, and readability features of particular news. Language driven model requires an approach to handle time-consuming and handcrafted features problems in order to deal with the curse of dimensionality problem. Therefore, the neural-based sequential learning model is used to achieve superior results for fake news detection. The results are drawn to validate the importance of the linguistic model extracted features and finally combined linguistic feature-driven model is able to achieve the average accuracy of 86% for fake news detection and classification. The sequential neural model results are compared with machine learning based models and LSTM based word embedding based fake news detection model as well. Comparative results show that features based sequential model is able to achieve comparable evaluation performance in discernable less time.

Linguistic feature based learning model for fake news detection and classification

Users of social media websites tend to rapidly spread breaking news and trending stories without considering their truthfulness. This facilitates the spread of rumors through social networks. A rumor is a story or statement for which truthfulness has not been verified. Efficiently detecting and acting upon rumors throughout social networks is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this paper, we study the problem of detecting breaking news rumors, instead of long-lasting rumors, that spread in social media. We propose a new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors. The proposed strategy is simple but effective to mitigate the topic shift issues. Emerging rumors do not have to be false at the time of the detection. They can be deemed later to be true or false. However, most previous studies on rumor detection focus on long-standing rumors and assume that rumors are always false. In contrast, our experiment simulates a cross-topic emerging rumor detection scenario with a real-life rumor dataset. Experimental results suggest that our proposed model outperforms state-of-the-art methods in terms of precision, recall, and F1.

Detecting breaking news rumors of emerging topics in social media

Topic models were proposed to detect the underlying semantic structure of large collections of text documents to facilitate the process of browsing and accessing documents with similar ideas and topics. Applying topic models to short text documents to extract meaningful topics is challenging. The problem becomes even more complicated when dealing with short and noisy micro‐posts in Twitter that are about one general topic. In such a case, the goal of applying topic models is to extract subtopics. This results in topics represented by similar sets of keywords, which in turn makes the process of topic interpretation more confusing. In this paper we propose a new method that incorporates Twitter‐LDA, WordNet, and hashtags to enhance the keyword labels that represent each topic. We emphasize the importance of different keywords to different topics based on the semantic relationships and the co‐occurrences of keywords in hashtags. We also propose a method to find the best number of topics to represent the text document collection. Experiments on two real‐life Twitter datasets on fashion suggest that our method performs better than the original Twitter‐LDA in terms of perplexity, topic coherence, and the quality of keywords for topic labeling.

Improving interpretations of topic modeling in microblogs

Users from all over the world increasingly adopt social media for newsgathering, especially during breaking news. Breaking news is an unexpected event that is currently developing. Early stages of breaking news are usually associated with lots of unverified information, i.e., rumors. Efficiently detecting and acting upon rumors in a timely fashion is of high importance to minimize their harmful effects. Yet, not all rumors have the potential to spread in social media. High-engaging rumors are those written in a manner that ensures achievement of the highest prevalence among the recipients. They are difficult to detect, spread very fast, and can cause serious damage to society. In this article, we propose a new multi-task Convolutional Neural Network (CNN) attention-based neural network architecture to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction in social media. The proposed model learns the salient semantic similarities among important features for detecting high-engaging breaking news rumors and separates them from the rest of the input text. Extensive experiments on five real-life datasets of breaking news suggest that our proposed model outperforms all baselines and is capable of detecting breaking news rumors and predicting their future popularity with high accuracy.

Detecting High-Engaging Breaking News Rumors in Social Media

The quality of education in a specific educational institution is directly reflected in the outcomes of their system. Higher-quality educational systems continue to deliver better learning experiences to enrolled students and better-developed skills and knowledge. To provide high-quality education, an institution must continually monitor its plans, update its courses’ topics and curriculum, and improve teaching facilities and different learning experiences. Students’ opinions and feedback regarding different aspects of a course and their personal learning experience, if properly gathered and analyzed, can be strong indicators of the quality of that course and help identify the areas of satisfaction and dissatisfaction with that course. Highlighting the strengths and weaknesses of each course helps faculty members put, execute, and evaluate a course quality improvement plan in the following semester. Such valuable students’ feedback and opinions about courses are scattered throughout different social media platforms and managed by different discussion groups, usually students. Thus, gathering honest and freely written comments and opinions in one place is challenging. Furthermore, extracting and analyzing courses’ quality and learning experience-related posts is not a trivial task. This study describes the process of designing and developing a smartphone application utilizing Sentiment Analysis techniques to address the problem of gathering, analyzing, and understanding students’ feedback and comments regarding different aspects of courses quality provided by an educational institution. The project’s primary goal is to benefit from student feedback regarding the institution’s courses to continuously assess and monitor the quality of the courses and the students’ learning experiences. A sample representative dataset of students’ unstructured free-text comments and answers to open-ended questions about five different courses over four consecutive semesters was collected, cleaned, and used to develop and test two sentiment analysis models: Naive Bayes in WEKA and a sentiment lexicon-based model named VADER. To further analyze and assess different aspects of the learning experience and courses along with its overall quality, answers to closed-end questions were also analyzed using the 5-point Likert scale. Preliminary results obtained from evaluating the sentiment analysis models show that the Naïve Bayes model achieved 68.7%, 68.8%, 68.8%, and 68.8%, while the VADER model achieved 72.12%, 72.82%, 72.12%, 71.87%, in terms of accuracy, precision, recall, and F1-score, respectively. Performance testing results of the application show that the maximum usage for the CPU is 44%, for the memory is 119 MB, for sending a request on the network 14.7 KB/s, for receiving a response is 226.5 KB/s, and the maximum energy usage is medium. For stress testing, obtained results show that the application can successfully deal with a maximum of 500 random, fast, and abnormal events. For user acceptance testing, users were surveyed to measure their level of satisfaction with the application using the system usability scale. The results show that 100% of users either agreed or strongly agreed that they would like to use the application and be more engaged in assessing the quality of courses. They also indicated that the application is easy to use, quick, and easy to learn. This paper also highlights various challenges and limitations developers face, along with important recommendations for further improvements and future work directions.

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A sentiment analysis-based smartphone application to continuously assess students’ feedback and monitor the quality of courses and the learning experience in educational institutions

Sarah A. Alkhodair

Papers

Detecting breaking news rumors of emerging topics in social media

Improving interpretations of topic modeling in microblogs

Detecting High-Engaging Breaking News Rumors in Social Media

A sentiment analysis-based smartphone application to continuously assess students’ feedback and monitor the quality of courses and the learning experience in educational institutions