This paper studies the dynamics of opinion formation and polarization in social media. We investigate whether users' stance concerning contentious subjects is influenced by the online discussions they are exposed to and interactions with users supporting different stances. We set up a series of predictive exercises based on machine learning models. Users are described using several posting activities features capturing their overall activity levels, posting success, the reactions their posts attract from users of different stances, and the types of discussions in which they engage. Given the user description at present, the purpose is to predict their stance in the future. Using a dataset of Brexit discussions on the Reddit platform, we show that the activity features regularly outperform the textual baseline, confirming the link between exposure to discussion and opinion. We find that the most informative features relate to the stance composition of the discussion in which users prefer to engage.
翻译:本文研究社交媒体舆论形成和两极分化的动态。 我们调查用户对争议议题的立场是否受到他们接触不同立场的在线讨论和与支持不同立场的用户互动的影响。 我们根据机器学习模式建立了一系列预测练习。 用户在介绍时使用几种张贴活动特征,捕捉他们的整体活动水平,张贴成功信息,他们的文章吸引不同立场的用户的反应,以及他们参与的讨论类型。 根据用户目前的描述,目的是预测他们今后的立场。 我们利用布雷希特在Redddit平台上的讨论数据集,显示活动的特点经常超过文字基线,确认接触讨论和意见之间的联系。 我们发现,最丰富的特征与用户喜欢参与的讨论的立场构成有关。