AbstracteCoaching platforms have become powerful tools to support users in their day-to-day physical routines. More and more research works show that motivational factors are strictly linked with the user inclination to share her fitness achievements on social media platforms. In this paper, we tackle the problem of analyzing and modeling users’ contextual information and real-time training data by exploiting state-of-the-art classification algorithms, to predict if a user will share her current running workout on Facebook. By analyzing user’s performance, collected by means of an eCoaching platform for runners, and crossing them with contextual information such as the weather, we are able to predict with a high accuracy if the user will post or not on Facebook. Given the positive impact that social media posts have in these scenarios, understanding what are the conditions that lead a user to post or not, can turn the output of the classification process into actionable knowledge. This knowledge can be exploited inside eCoaching platforms to model user behavior in broader and deeper ways, to develop novel forms of intervention and favor users’ motivation on the long term.

中文翻译：

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对来自 eCoaching 平台中锻炼的实时数据和上下文信息进行建模，以预测用户在 Facebook 上的分享行为

AbstracteCoaching 平台已成为支持用户日常锻炼的强大工具。越来越多的研究表明，激励因素与用户在社交媒体平台上分享健身成果的倾向密切相关。在本文中，我们通过利用最先进的分类算法来解决分析和建模用户上下文信息和实时训练数据的问题，以预测用户是否会在 Facebook 上分享她当前的跑步锻炼。通过分析用户的表现，通过跑步者的 eCoaching 平台收集，并将它们与上下文信息（如天气）交叉，我们能够高精度地预测用户是否会在 Facebook 上发帖。鉴于社交媒体帖子在这些情况下产生的积极影响，了解导致用户发布或不发布的条件是什么，可以将分类过程的输出转化为可操作的知识。可以在 eCoaching 平台内利用这​​些知识，以更广泛和更深入的方式对用户行为进行建模，以开发新颖的干预形式并从长远来看有利于用户的动机。