Many of the interactions between users on social networks are controversial, specially in polarized environments. In effect, rather than producing a space for deliberation, these environments foster the emergence of users that disqualify the position of others. On news sites, comments on the news are characterized by such interactions. This is detrimental to the construction of a deliberative and democratic climate, stressing the need for automatic tools that can provide an early detection of polarization and controversy. We introduce GENE (graph generation conditioned on named entities), a representation of user networks conditioned on the named entities (personalities, brands, organizations) which users comment upon. GENE models the leaning that each user has concerning entities mentioned in the news. GENE graphs is able to segment the user network according to their polarity. Using the segmented network, we study the performance of two controversy indices, the existing Random Walks Controversy (RWC) and another one we introduce, Relative Closeness Controversy (RCC). These indices measure the interaction between the network’s poles providing a metric to quantify the emergence of controversy. To evaluate the performance of GENE, we model the network of users of a popular news site in Chile, collecting data in an observation window of more than three years. A large-scale evaluation using GENE, on thousands of news, allows us to conclude that over 60% of user comments have a predictable polarity. This predictability of the user interaction scenario allows both controversy indices to detect a controversy successfully. In particular, our introduced RCC index shows satisfactory performance in the early detection of controversies using partial information collected during the first hours of the news event, with a sensitivity to the target class exceeding 90%.

社交网络上用户之间的许多交互都是有争议的，尤其是在两极分化的环境中。实际上，这些环境不是在提供审议的空间，而是促进了使其他人丧失资格的用户的涌现。在新闻站点上，新闻评论的特点是这种互动。这不利于营造协商和民主的气氛，强调了对自动工具的需求，这些工具可以提早发现两极分化和争议。我们引进GENE（g ^拍摄和g ^ ê neration空调上ñ艾湄é实体），以用户评论的命名实体（个性，品牌，组织）为条件的用户网络表示。GENE模拟每个用户对新闻中提到的实体的偏好。GENE图能够根据用户网络的极性对其进行细分。使用分段网络，我们研究了两个争议指标的性能，即现有的随机游走争议（RWC）和我们引入的另一个指标，相对亲密性争议（RCC）。这些指标衡量了网络两极之间的互动，从而提供了一种量化争议出现的度量。评估GENE的性能，我们为智利一个热门新闻站点的用户网络建模，在超过三年的观察期中收集数据。使用GENE对数千条新闻进行的大规模评估，使我们得出结论，超过60％的用户评论具有可预测的极性。用户交互场景的这种可预测性允许两个争议索引成功检测到争议。尤其是，我们引入的RCC索引使用新闻事件的第一个小时收集的部分信息，在早期发现争议中表现出令人满意的性能，对目标类别的敏感性超过90％。