Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach Subject The topic of the resource Classification accuracy; Cyber attacks; Fact checking; Feature extraction; Hist gradient boosting classifier; Machine learning; Social bot detection Description An account of the resource There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents: A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection. And Harris Hawk optimization with Bi-LSTM for social bot prediction. Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset. 2023 Creator An entity primarily responsible for making the resource Sethurajan M.R.; K? N. Source A related resource from which the described resource is derived MethodsX, Vol-11 Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.mex.2023.102430" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.mex.2023.102430</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173865720&amp;doi=10.1016%2Fj.mex.2023.102430&amp;partnerID=40&amp;md5=37d425003a5e15857a8642332f6ae869" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173865720&amp;doi=10.1016%2fj.mex.2023.102430&amp;partnerID=40&amp;md5=37d425003a5e15857a8642332f6ae869</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 22150161 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sethurajan M.R., Research Schol, Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560029, India; K? N., Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560029, India