# Measuring Polarization in Online Debates
## 1 Introduction  
Nowadays, there is growing controversy regarding the emergence of polarization in discussions on social networks, and the responsibility of companies in this problem. For example, Facebook researchers have studied the spread of divisive content on the platform [1, 2]. One of their findings is that users with hyperactive engagement are far more partisan on average than normal users. Facebook has launched some initiatives to try to mitigate the factors that may be helping the spread of such content [3]. For example, they re-calibrated the prioritization of content in users' News Feeds to give more preference to posts of friends and family over news content, justified by the findings that suggest that people derive more \"meaningful social interactions\", or MSI, when they engage with people they know, although it is not clear what exactly comprises a meaningful social interaction for Facebook. Other actions include improving the detection of fake accounts and removing recommendations of pages that violate Facebook Community Standards or are rated false by their fact checkers. However, the findings of Facebook suggest that completely fixing the polarization problem may be difficult. As they commented in an internal company presentation [4], Facebook algorithms exploit the human brain's attraction to divisiveness and tend to feed users with more and more divisive content, which seems to ensure that users will spend more time on the platform. Publishers and political parties reorient their posts toward outrage and sensationalism since this produces high levels of comments and reactions. Some political parties in Europe event told Facebook that the algorithm madethem shift their policy positions. However, because engagement is of course fundamental for the profit of the company, social network companies should be cautious to not react disproportionately when trying to mitigate polarization. In spite of all these issues, it is worth noticing that it is not clear how much responsibility we can give to social networks on these problems. For example, there is some work that indicates that, at least for polarization in people's views of political parties, the increased use of social networks and the internet may not be necessarily increasing polarization [5]. Putting aside whether specific social network platforms are more or less responsible for polarization, it is clear that what companies, or policymakers, can do is to define more transparent ways to monitor such possible non-desirable behaviors so that we can decide to act only in situations where there is some objective measure that polarization is taking place and to a certain level of severity. Previous work has studied the presence of polarization in different concrete examples, trying to analyze the relevant characteristics in these cases. For example, the works [6; 7] studied the emergence of so-called echo chambers, where users are found to interact mainly only with the users that agree with them and to have very few interactions with other groups. However, online discussions in social networks can also show polarization where there are answers with negative sentiment between different groups, which can be considered the most worrying situation. For example, in [8] they studied hyperpartisanship and polarization in Twitter during the 2018 Brazilian presidential election. Their findings showed that there was more interaction within each group (pro/anti [PERSON]) but there was also an interaction between both groups. Actually, there are also cases where the interaction between groups can be more relevant than those within groups, like in the case studied in [9] where the analysis of the 2016 U.S. elections on Reddit showed a significant number of interactions between pro-Trump and pro-Hillary supporters. It is worth noting that the existence of polarization in debates could be partially explained as a consequence of the spiral of silence theory [10].