Fake news can confuse many people in the area of politics, culture, healthcare, etc. Fake news refers to news containing misleading or fabricated contents that are actually groundless; they are intentionally exaggerated or provide false information. As such, fake news can distort reality and cause social problems, such as self-misdiagnosis of medical issues. Many academic researchers have been collecting data from social and medical media, which are sources of various information flows, and conducting studies to analyse and detect fake news. However, in the case of conventional studies, the features used for analysis are limited, and the consideration for newly added features of social media is lacking. Therefore, this study proposes a fake news analysis modelling method by identifying a variety of features and collecting various data from Twitter, a social media outlet with a good deal of power in terms of spreading information. The method proposed in this study can increase the accuracy of fake news analysis by acquiring more potential information from the Quote Retweet feature added to Twitter in 2015, compared to the more conventional and common Retweet only. Furthermore, fake news was analysed through neural network-based classification modelling by using the preprocessed data and the identified best features in the learning data. In the performance results, using the neural network-based classifier, the classification model that also used Quote Retweet, showed an improvement in performance over the conventional methods, and it was confirmed that the identified best features had a significant impact on increasing the classification accuracy of fake news.

Artificial intelligence is one of the key technologies for progression to the fourth industrial revolution. This technology also has a significant impact on software professionals who are continuously striving to achieve high-quality software development by fixing various types of software bugs. During the software development and maintenance stages, software bugs are the major factor that can affect the cost and time of software delivery. To efficiently fix a software bug, open bug repositories are used for identifying bug reports and for classifying and prioritizing the reports for assignment to the most appropriate software developers based on their level of interest and expertise. Owing to a lack of resources such as time and manpower, this bug report triage process is extremely important in software development. To improve the bug report triage performance, numerous studies have focused on a latent Dirichlet allocation (LDA) using the k-nearest neighbors or a support vector machine. Although the existing approaches have improved the accuracy of a bug triage, they often cause conflicts between the combined techniques and generate incorrect triage results. In this study, we propose a method for improving the bug report triage performance using multiple LDA-based topic sets by improving the LDA. The proposed method improves the existing topic sets of the LDA by building two adjunct topic sets. In our experiment, we collected bug reports from a popular bug tracking system, Bugzilla, as well as Android bug reports, to evaluate the proposed method and demonstrate the achievement of the following two goals: increase the bug report triage accuracy, and satisfy the compatibility with other state-of-the-art approaches.

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait.” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.