been identified as a major threat on online social media platforms. Pew Research Center [13] reports that among 4248 adults in the United States, 41% have personally experienced harassing behavior online, whereas 66% witnessed harassment directed towards others. Around 22% of adults have experienced offensive name-calling, purposeful embarrassment (22%), physical threats (10%), and sexual harassment (6%), among other types of harassment. Social media platforms are the most prominent grounds for such toxic behavior. Even though they often provide ways of flagging offensive and hateful content, only 17% of all adults have flagged harassing conversation, whereas only 12% of adults have reported someone for such acts [13].

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As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification-Random Forest, K-Nearest Neighbor, and Neural Network-to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.

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Abstract-The Danish EDISON project has been launched to investigate how a large fleet of electric vehicles (EVs) can be integrated in a way that supports the electric grid while benefitting both the individual car owners and society as a whole through reductions in CO2 emissions. The consortium partners include energy companies, technology suppliers and research laboratories and institutes. The aim is to perform a thorough investigation of the challenges and opportunities of EVs and then to deliver a technical platform that can be demonstrated on the Danish island of Bornholm. To reach this goal, a vast amount of research is done in various areas of EV technology by the partners. This paper will focus on the ICT-based distributed software integration, which plays a major role for the success of EDISON. Key solution technologies and standards that will accommodate communication and optimize the coordination of EVs will be described as well as the simulation work that will help to reach the goals of the project.

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