Reviewer has chosen not to be AnonymousOverall Impression:
RejectTechnical Quality of the paper:
Incomplete or inappropriateNovelty:
Limited noveltyData availability:
Not all used and produced data are FAIR and openly available in established data repositories; authors need to fix thisLength of the manuscript:
The length of this manuscript is about right
Summary of paper in a few sentences:
This paper discusses the effect of personalisation technologies on polarisation.
Reasons to accept:
1.- The paper addresses a relevant topic, that of the effect of personalisation technologies on polarisation
2.- The highlights important points of the debate
3.- The paper proposes an abstraction of the problem, identifying three levels or dimensions: individual, local and global
Reasons to reject:
1.- The paper addresses the problem mainly from a social science perspective, sidelining the technological and data science perspectives. In this sense, the paper may not fit well the topics of the journal
2.- The paper presents a wide range of concepts in an ambiguous manner. For example, recommendation algorithms, and personalisation algorithms are not the same, and acknowledging this distinction is important for the problem being discussed. Similarly with the effect of personalisation algorithms vs. the effect of communication via social networks.
3.- The paper seems to be missing important literature from computer science and from computational social science research.
The paper addresses an important topic and discusses important points within the debate. Is is also well written and structured. However, multiple important issues are not being considered in this discussion.
First, please note that there are important distinctions between personalisation, recommendation, and the effect of social networks. Communication via social networks have enabled us to connect with individuals that are physically far from us, to be exposed to high amounts of information from a variety of sources, to hide on the anonymity of user accounts, etc. These are aspects of the communication medium that may incentivise polarisation, but these aspects are different than the algorithmic aspects of personalisation or recommendation methods.
At no point in the paper the algorithmic aspects are being discussed. What are the particularities of collaborative filtering, matrix factorisation, content-based, or other recommendation methods that may increase polarisation? How can different user profiles, item profiles or matching methods may influence polarisation? How can the data that is being personalised influence polarisation?
It is also important to consider that 'social connection' does not mean influence. Users may receive information that does not influence them in any way. Note that some users are more prone to polarisation than others. There is important research on the field of misinformation related to the topics discussed in this article, including the effects of confirmation biases, or the effect of human values and personality types on the spread of misinformation and polarisation.There are also relevant works on social media 'influence' and engagement. Note that not all users have the same influence than others (authorities, celebrities, etc.) have a higher degree of influence. They also tend to have more followers and hence higher chances to spread their messages.
These features, such as the number of followers, as well as network structures and information cascades, have been studied in the context of multiple election campaigns, on the spread of misinformation and in the context of radicalisation studies. However, the authors mentioned that local-level aspects of communication and their effects on polarisation have not been considered. I would recommend the authors to take a look to the work of Filippo Menczer, Emilio Ferrara or Claudia Wagner.
In summary, while this paper targets an important problem, and brings important aspects into discussion, the article seems to miss the technical angle, as well as a wider review of technical works from the computer science and computational social science fields. Moreover, the article does not seem to provide a novel and potentially disruptive view of the topic (as required for position papers https://datasciencehub.net/content/guidelines-reviewers) but more of an overview and an in depth discussion of the different aspects of the topic.