... misinformation data. The ten most frequently used datasets are identified. Most studies (96.1%) developed or applied machine learning, especially deep learning models. The most advanced current misinformation detection models could achieve pretty high performance. For example, among 104 studies reporting...

... © Emerald Publishing Limited 2021 Emerald Publishing Limited Licensed re-use rights only Artificial intelligence Agriculture Precision agriculture Machine learning Decision support system Deep learning Agriculture has been an essential component of social and economic...

... The present study applied econometric fixed-effect (FE) regression analysis and random forest (RF) algorithm through machine learning for comprehensive estimations in achieving SPS. This study gauges the nexus between the EGD as an independent variable and public service sustainability (PSS) as a proxy...

..., demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques. Graphical approaches can store data in the form of triplets that contain two entities...

.../approach The study proposes a methodology based on strong sentiment classification using machine learning and an advanced scoring technique. Findings The results show the following key findings: the proposed methodology is able to automatically capture trending topics and achieve better classification...

... users. We refer to this data set as the ZA2018 data set. Social media Machine learning Twitter Public health Data science In recent years, social media platforms such as discussion forums and social networks have been growing rapidly as a channel for the communication and engagement...

..., individual or organization user, academic or non-academic background, and psychology/political science domain knowledge background. A machine learning approach using support vector machine (SVM) was used for user classification based on the Twitter user profile information. Latent Dirichlet allocation (LDA...

...Mike Thelwall Purpose The purpose of this paper is to investigate whether machine learning induces gender biases in the sense of results that are more accurate for male authors or for female authors. It also investigates whether training separate male and female variants could improve the accuracy...

... the frequencies in the text of each feature in a specified feature set. Some machine learning algorithm is then used to learn a classifier that best distinguishes among training examples in different classes. These classifiers can then be used to classify new texts. The effectiveness of this method can...