Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The term "other Women" in the Abstract may benefit from further clarification or correction for accuracy. The same phrase appears on page 2, line 68.It is recommended to explicitly mention the current name of the social media platform (X, formerly Twitter) at least at the beginning of the article. This ensures precision and clarity for readers.When discussing data journalism, consider incorporating references to the sociocultural context in which it emerged. Exploring works that differentiate data journalism in the context of big data, especially within the realm of Social Media, can provide valuable contextual depth.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Overall, the authors present an original paper that analyses the differences between men and women in political journalism and data journalism. It focuses on journalists' networking patterns on Twitter.

The authors could improve the contextualisation and focus of their contribution. In my view, both the way in which the data on political journalism have been sampled and the corresponding analysis allow only cautious conclusions to be drawn.

The first comments on Lippmann and Lewin are quite detailed, but not very closely related to the paper. Other aspects that would be more important for contextualisation are missing. I recommend that the authors consider these aspects more in their introduction and in the literature review. I suggest that the following points be included:

1) What particular circumstances in journalism can contribute to homophily among journalists? Which field-specific contexts are relevant?

2) The formation of networks in journalism depends on many factors and mechanisms. These should be identified more clearly.

The different possibilities of homophily should therefore be discussed more in relation to journalism. This paper rightly focuses on gender homophily. However, there are many other pressures/constraints on journalists.

Interactions between journalists are associated with patterns of strong co-orientation and also dependencies, e.g. through competition. This also exists outside of Twitter and in this respect it is likely that the basic patterns are also visible on Twitter.

The research on Twitter and journalism is well presented.

In general, the specifics of the journalistic beat should be taken into account. Journalists who cover politics tend to be older, as journalism studies show. The data journalism beat is much younger. Therefore, it is likely to have a higher proportion of women, as the gender ratio is now more balanced, especially in the younger age groups. This could also affect the likelihood of interacting with (other) women.

Differences in activity between men and women may also be linked to developments on Twitter. Female journalists are more likely to complain about negative experiences on the platform, which may have an impact on their activity.

In terms of method and questions, I believe there is room for improvement. The originality, coherence and clarity could be strengthened by addressing the following aspects:

- It is unclear from the sample where the journalists originate from and what their classification is. I assume that this is also important for the interpretation. For example, were journalists from public broadcasting included or not? I also wonder whether the data journalists could have been researched in a more comparable way to your selection strategy for journalists covering politics. In one case you look more systematically for outlets and journalists in the political beat, in the other you sample from a working group.

- Please always report the number of nodes and edges for each graph. This can also be useful for network tables. For the interpretation of some measures, it is important to know whether the networks differ greatly in size. How many journalists are in each network?

- Hypotheses H2/H3 should rather analyse the aspect of homophily. The fact that women are mentioned less in absolute terms is mainly due to their much smaller presence in the network. In their current form, the hypotheses provide rather limited insights.

- The analysis of H2/H3 using cross-tabulations is appropriate and clear. However, it should be extended to include the network perspective. In this way, gender homophily could be calculated at group and network level (E-I index).

- In my opinion, the assessment of H4 and H5 by looking at the top 30 hashtags and top 30 accounts (line 428) is not sufficient for an adequate clarification of these hypotheses. Against this background, the conclusions at the end of the paper (line 576) are perhaps too far-reaching?

- The network analyses (community detection, self-retweeting) and especially the network metrics are interesting, but do not centrally answer questions RQ1-3. In my opinion, it would make sense to develop an RQ for this, so that the questions and the presentation of the results match.

 

- The layout algorithms used for visualisation could be specified for the network figures.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript has been significantly improved after the revision.