Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts from this grey literature can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by creating the patterns of the security knowledge gained from different sources. Prior studies neither systematically analysed the wide range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Moreover, existing topic modelling methods are not capable of identifying the cybersecurity concepts completely and the generated topics considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories to analyse trending topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings as follows: (1) The impact reflected from cybersecurity texts strongly correlates with the monetary loss caused by cybercrimes, (2) security blogs have produced the context of cybersecurity most intensively, and (3) websites deliver security information without caring about timeliness much.

Tendencias de investigación en arquitectura entre 2016-2020 en la base de datos SCOPUS y su relación con la creación de grupos de investigación

The interest in making research more dynamic within universities has evidenced the need for the creation of research groups. In this sense, the purpose of this article is to establish a relationship between the data obtained from bibliometrics and the profile and interest of Architecture Faculty researchers, to identify research niches that guide their work. This research was carried out through a systematic review and use of scientific production bibliometric tools in the last five years within the architecture domain in the SCOPUS database. The research made used five steps that guided the process: search; evaluation; synthesis; analysis; and monitoring. Through the search process, 1,465 scientific documents were obtained, analyzed with the Bibliometrix web application using indicators that allowed obtaining the results, to connect them to both teaching interests and profiles. The analysis identified that sustainability, design, and energy efficiency are topics of interest and constitute trending topics to promote the work and the constitution of research groups.

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.

Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.

Emerging Research Topic Detection Using Filtered-LDA

Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic.

Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic

COVID-19 caused a significant public health crisis worldwide and triggered some other issues such as economic crisis, job cuts, mental anxiety, etc. This pandemic plies across the world and involves many people not only through the infection but also agitation, stress, fret, fear, repugnance, and poignancy. During this time, social media involvement and interaction increase dynamically and share one’s viewpoint and aspects under those mentioned health crises. From user-generated content on social media, we can analyze the public’s thoughts and sentiments on health status, concerns, panic, and awareness related to COVID-19, which can ultimately assist in developing health intervention strategies and design effective campaigns based on public perceptions. In this work, we scrutinize the users’ sentiment in different time intervals to assist in trending topics in Twitter on the COVID-19 tweets dataset. We also find out the sentimental clusters from the sentiment categories. With the help of comprehensive sentiment dynamics, we investigate different experimental results that exhibit different multifariousness in social media engagement and communication in the pandemic period.

Discovering social media topics and patterns in the coronavirus and election era

Purpose This study aims to understand the relationship between politics and pandemics in shaping the characteristics and themes of people’s Tweets during the US 2020 presidential election. Additionally, the purpose is to detect misinformation and extremism, not only to help online social networks (OSN) to target such content more rapidly but also to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN. This could help authorities to identify the intents behind them and find out how and when they should address such content. Design/methodology/approach This study focuses on extracting and verifying knowledge from large-scale OSN data, at the intersection of the Coronavirus pandemic and the US 2020 presidential election. More specifically, this study makes manual, statistical and automatic inferences and extracts knowledge from over a million Tweets related to the two aforementioned major events. On the other hand, disinformation operations intensified in 2020 with the coincidence of the Coronavirus pandemic and presidential election. This study applies machine learning to detect misinformation and extreme opinions on OSN. Over one million Tweets have been collected by our server in real-time from the beginning of April 2020 to the end of January 2021, using six keywords, namely, Covid, Corona, Trump, Biden, Democrats and Republicans. These Tweets are inspected with regard to their topics, opinions, news, and political affiliation, along with misinformation and extremism. Findings Our analyses showed that the majority of these Tweets concern death tolls, testing, mask, drugs, vaccine, and travel bans. The second concern among these Tweets is reopening the economy and schools, unemployment, and stimulus bills. The third concern is related to the Coronavirus pandemic’s impacts on politics, voting, and misinformation. This highlights the topics that US voters on Twitter were most concerned about during this time period, among the multitude of other topics that politicians and news media were reporting or discussing. Automatic classification of these Tweets using a long short-term memory network revealed that Tweets containing misinformation formed between 0.5% and 1.1% of Coronavirus-related Tweets every month and Tweets containing extreme opinions formed between 0.5% and 3.1% of them every month, with its pick in October 2020, coinciding with the US presidential election month. Originality/value The originality of this study lies in establishing a framework to collect, process, and classify OSN data to detect misinformation and extremism and to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN.