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Peer-Review Record

Social Media Metrics as Predictors of Publishers’ Website Traffic

Journal. Media 2024, 5(1), 281-297; https://doi.org/10.3390/journalmedia5010019

by Ioannis Angelou^1,*

, Vasileios Katsaras²

, Dimitris Kourkouridis³

and Andreas Veglis¹

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3:

Frah Rukhsar Khan

Journal. Media 2024, 5(1), 281-297; https://doi.org/10.3390/journalmedia5010019

Submission received: 19 November 2023 / Revised: 12 January 2024 / Accepted: 28 February 2024 / Published: 4 March 2024

(This article belongs to the Special Issue The Situationship between Broadcasting and Streaming Platforms: Exploring the New Landscape of Media Usage)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

- I think there is an issue with the framing of this study as being news/journalism based, when the data being used may not be, as noted in the limitations, from news organizations. Assuming you can identify the organizations in the data, can you not just use the news organizations’ data if this is the way you want to frame this research? Can you at least identify how many of the organizations are news organizations to give a sense of the relevance of findings? News compared to other types of media, for example. If there were no significant differences in types of organizations, then it might be acceptable to use overall #s, but you have to explain why more clearly/identify where the data comes from. If news vs. other orgs is quite different and you want to keep this a large-scale study, as you noted in your limitations, you need to reframe this paper and use some different literature so that you are not centring discussion on journalistic practice.

-On page 4 you say” This big debate on the relationship between social and legacy news media” – however, there isn’t really a debate about the influence of social media on newsrooms, when examining current literature

-In general, the literature being cited is quite old – particularly re. metrics & analytics & social media as these are areas where there is ongoing and significant change (I have offered a number of suggestions for additions below). There is much more recent Hermida work that could be cited and Bruns is also an important scholar in this area absent in your review. Why, for example, are you using the 2018 Digital News Report vs. the 2023 report? If you are doing so to align with your data collection period, explain that, but also identify what might have changed since then using the more current report. The data itself are also dated – and is something that should be addressed as a limitation – It’s a particular issue that you’re using older data to frame the study as relevant to current practice – perhaps it’s better to frame it as looking at practice on a continuum and identifying that there have been significant changes in newsrooms etc., which you could also do with a more current lit review.

-In terms of methodology, “Data collected, alternatively called “social media metrics,” concern the activity on official Facebook pages and Twitter accounts of sample publishers” – is this further explanation of what the Brandwatch data encompasses? – at first read, it seems as if you might be referring to a third set of data – it might be an issue of sentence structure

-Charts, in general, are extremely difficult to read

-”During the survey, the number of page likes of the sample amounted to 173,103 on average for each page.” – Do you mean during the timeframe of data gathering, data in the survey during the specified period you were examining?

-There are significant issues with the writing, a few examples listed below:

-should be publishers’ in title

-this is awkward phrasing:

“underlines the need to shift research toward specific indicators as tools for evaluating practices that are followed (Zamith, 2018)” – practices followed by journalists with regard to implementation of social media?

-with a rare large-scale survey vs. with one of the on pg. 1

-Quote from Zuckerberg at top needs to be identified/formatted as such – there are other issues of lack of proper formatting for quoted material

-Significant issues with sentence structure: eg.” In their case study on publishers and platforms relation” – relationship? Or better, on the relationship between publishers and platforms

-Watch for typos, eg. Napoli misspelled in citation at top of page 3

-I’m not sure what you mean by this: “Our study examined the social media metrics and publishers' websites traffic relationship in the context of the relative encouragement of Ksiazek et al. (2014) and Wallace 162 (2017)” – do you mean that you are building on their work?

-”Our results showed that the number of comments on media posts on social media was related to the impact of their websites” – not sure what you mean by this, what is the impact of their websites/how is that being measured? Pg. 12

Suggested literature:

Bruns, A. (2021). Gatewatching and news curation. The Routledge Companion to Political Journalism. Routledge.

Hermida, A. (2020). Post-publication gatekeeping: The interplay of publics, platforms, paraphernalia, and practices in the circulation of news. Journalism & Mass Communication Quarterly, 97(2), 469-491.

Walters, P. (2022). Reclaiming control: How journalists embrace social media logics while defending journalistic values. Digital Journalism, 10(9), 1482-1501.

García-Perdomo, V. (2021). How social media influence TV newsrooms online engagement and video distribution. Journalism & Mass Communication Quarterly, 10776990211027864.

Tenor, C., 2023. Metrics as the new normal–exploring the evolution of audience metrics as a decision-making tool in Swedish newsrooms 1995-2022. Journalism, p.14648849231169185.

Zamith, R., Belair-Gagnon, V. and Lewis, S.C., 2020. Constructing audience quantification: Social influences and the development of norms about audience analytics and metrics. New Media & Society, 22(10), pp.1763-1784.

Dodds, T., de Vreese, C., Helberger, N., Resendez, V., & Seipp, T., 2023. Popularity-driven Metrics: Audience Analytics and Shifting Opinion Power to Digital Platforms. Journalism Studies, 1-19.

Comments on the Quality of English Language

Significant editing is required.

Author Response

Thank you for your insightful comments. We appreciate the opportunity to provide further clarification on our research sample and methodology.

Our primary aim was to develop prediction models for website traffic based on social media metrics, and to achieve this, we meticulously designed our research. Social media metrics are inherently public and accessible, making them a feasible and widely used source of data. However, obtaining precise and accurate data on website traffic indicators poses a challenge. To address this, we chose to work with the Online Publishers Association of Greece, a reputable organization that provides accurate and comparable data verified by a reliable third party.

It is crucial to note that our study focuses on examining the network gatekeeping theory and the reciprocal journalism theoretical concept within a research sample of content publishers. Specifically, the Online Publishers Association of Greece represents companies that primarily produce original digital content, rather than simply aggregating third-party content. Therefore, it is accurate to characterize our research sample as consisting of media outlets dedicated to journalistic content.

To enhance transparency and provide readers with a more nuanced understanding of our sample, we have added a clarification in the methodology section. The addition specifies that the majority of the included websites are news-focused, covering a range of topics such as sports news, travel journalism, celebrity and lifestyle journalism, fashion journalism, food journalism, men's and women's magazines, and weather news:

“The vast majority of the included websites are news-focused, covering a range of topics such as “hard” news, sports news, travel journalism, celebrity and lifestyle journalism, fashion journalism, food journalism, men's and women's magazines, and weather news.”

While we acknowledge the potential importance of separating our sample into different media groups, we encountered a limitation in the number of outlets within each group, which would hinder our ability to run meaningful statistical tests such as regression analysis.

We believe that these clarifications address your concerns and provide a more comprehensive context for the nature of our research sample. We hope that these adjustments contribute to a more accurate interpretation of our study.

In the mentioned section, we aimed to convey the evolving nature of the discourse surrounding the relationship between social and legacy news media, particularly focusing on the challenges posed by metrics in the contemporary social media landscape. We appreciate the opportunity to clarify this point and acknowledge that the term "debate" might be nuanced. The provided additional context emphasizes the ongoing deliberation within the literature on how metrics, audience engagement, and evolving business models impact newsroom practices. These dynamics contribute to a broader conversation within the field, and our intent was to highlight the complex and multifaceted nature of this ongoing discourse.

“In recent years, the use of metrics challenges aligning audience and journalist values in the metric-centric social media environment (Tenor, 2023). Managers perceive a new-found alignment between audience metrics and professional news selection. Commercial publishers prioritize the shift to subscribers, placing pressure on individual journalists. Public service broadcasting finds the link between journalists' performance and revenue in commercial newspapers extreme. Despite growing emphasis on audience engage-ment, news organizations persist in upholding traditional news values (Walters, 2022), reflecting tension between relinquishing and retaining control over content and ethical standards (Lewis, 2012). While Hermida (2020) highlights the role of algorithms in acting as gatekeeping mechanisms on platforms by selecting and suggesting news and in-formation, Dodds (2023) points out three competing influences: journalists' perceptions, user preferences inferred from software-collected data, and platforms' pursuit of viral content.”

We have taken your suggestions into account and have made several revisions to the literature cited in our manuscript. We have included more recent works, addressing the ongoing and significant changes in the areas of metrics, analytics, and social media. Additionally, we updated the data using the 2023 Digital News Report to align it with our data collection period.

You can see the part we added on “The Greek case’s special interest” section below:

“Based on the 2023 Digital News Report, the most frequently accessed source of news in Greece is online (social media included), which accounts for 81% of sources of news consumption, followed by social media (61%). These percentages significantly exceed those of TV (48%) and print (15%) (Newman et al., 2023).”

To address the temporal nature of our data, we have included a section in the limitations explicitly acknowledging this aspect. We recognize that the media landscape is continually evolving, and our findings may not fully capture the most recent developments in newsroom practices. We now emphasize that our study serves as a snapshot of practices during the specific period covered by the dataset, providing insights into a particular timeframe.

“One notable limitation of this study is the temporal nature of the data utilized. The dataset employed for our analysis reflects a specific timeframe, and as such, the findings may not fully encapsulate the most recent developments in newsroom practices. We acknowledge that the media landscape is subject to continuous evolution, and the data's age could impact the generalizability of our conclusions to the current media environ-ment. To address this limitation, we emphasize that our study serves as a snapshot of practices during the specific period covered by the dataset.”

Newman, N., Fletcher, R., Eddy, K., Robertson, C.T., and Nielsen, R.K. (2023). Reuters Institute Digital News Report 2023. Oxford, UK: Reuters Institute for the study of journalism. University of Oxford. Available at: https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2023-06/Digital_News_Report_2023.pdf (accessed 10 January 2024)

Thank you for your observation. We have revised the sentence to enhance clarity and explicitly state that the data collected, referred to as "social media metrics," is indeed gathered by Brandwatch. The updated sentence now reads:

“Data collected by Brandwatch, alternatively called “social media metrics,” concern the activity on official Facebook pages and Twitter accounts of sample publishers.”

-Charts, in general, are extremely difficult to read

Thank you for the feedback. We have made improvements to enhance the readability of the charts and tables in our manuscript. The revised presentation aims to provide clarity and facilitate easier comprehension of the data.

In response to your comment, the rephrased statement now reads:

"During the survey period, the sample's average number of page likes amounted to 173,103 for each page."

This revision clarifies that the mentioned number of page likes pertains to the specific period examined in the survey, addressing any potential confusion about the timeframe of data gathering.

-There are significant issues with the writing, a few examples listed below:

We appreciate your feedback, and based on your comments, we have conducted a thorough proofreading of the entire paper to address any writing issues. We believe that the revised version will significantly improve the overall quality of the writing.

-should be publishers’ in title

The title has been revised.

-this is awkward phrasing:

We have addressed and fixed this sentence.

-with a rare large-scale survey vs. with one of the on pg. 1

We have corrected this sentence

-Quote from Zuckerberg at top needs to be identified/formatted as such – there are other issues of lack of proper formatting for quoted material

We have now formatted suitably the relevant part.

-Significant issues with sentence structure: eg.” In their case study on publishers and platforms relation” – relationship? Or better, on the relationship between publishers and platforms

Corrections have been made to this sentence.

-Watch for typos, eg. Napoli misspelled in citation at top of page 3

We have now undertaken a thorough proofreading of the entire paper, and we hope that we have addressed most of these issues.

We have addressed and fixed this sentence.

As we mention on the Results section:
“The present study found a positive correlation between the number of audience comments on both Facebook and Twitter (audience replies), with and the number of visits and page views on publishers' websites.”

The revised response aims to clarify the connection between social media comments and website performance:
“Our results showed that the quantity of comments on social media posts by the audience correlated with the performance of their respective websites.”

Suggested literature:

Bruns, A. (2021). Gatewatching and news curation. The Routledge Companion to Political Journalism. Routledge.

Walters, P. (2022). Reclaiming control: How journalists embrace social media logics while defending journalistic values. Digital Journalism, 10(9), 1482-1501. https://doi.org/10.1080/21670811.2021.1942113

García-Perdomo, V. (2021). How social media influence TV newsrooms online engagement and video distribution. Journalism & Mass Communication Quarterly. https://doi.org/10.1177/10776990211027864

Tenor, C. (2023). Metrics as the new normal – exploring the evolution of audience metrics as a decision-making tool in Swedish newsrooms 1995-2022. Journalism, 0(0). https://doi.org/10.1177/14648849231169185

Dodds, T., de Vreese, C., Helberger, N., Resendez, V., & Seipp, T., 2023. Popularity-driven Metrics: Audience Analytics and Shifting Opinion Power to Digital Platforms. Journalism Studies, 1-19. https://doi.org/10.1080/1461670X.2023.2167104

We have taken into account the literature you recommended, and we have incorporated nearly all of the suggested works. Analytically:

García-Perdomo, V. (2021). How social media influence TV newsrooms online engagement and video distribution. Journalism & Mass Communication Quarterly. https://doi.org/10.1177/10776990211027864

Dodds, T., de Vreese, C., Helberger, N., Resendez, V., & Seipp, T., 2023. Popularity-driven Metrics: Audience Analytics and Shifting Opinion Power to Digital Platforms. Journalism Studies, 1-19. https://doi.org/10.1080/1461670X.2023.2167104

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper titled "Social media metrics as predictors of publishers websites traffic” purports to investigate the relationship between social media metrics and website traffic for publishers. While the paper presents an intriguing exploration of the topic, a closer examination reveals potential shortcomings in its argumentation, particularly concerning the issue of causality.

One of the key assertions made by the authors is that social media metrics can act as predictors of website traffic, implying a causal relationship. However, a careful analysis of the methodology employed in the study suggests that the research primarily focuses on establishing correlations rather than causation. Thisis only shortly mentioned in the limitations section. Although correlation analyses are valuable for understanding patterns and relationships, they do not inherently establish a cause-and-effect relationship. Causality requires a more rigorous approach, such as time-series analysis, which the paper at hand seems to lack. It is plausible that the relationship is bidirectional, suggesting a spillover effect where increased website traffic may, in turn, influence social media metrics. For instance, a surge in website visitors might lead to amplified social media interactions, such as more shares, likes, or comments. This reciprocal influence challenges the simplistic interpretation of a unidirectional causation from social media metrics to website traffic. Acknowledging the potential existence of this feedback loop is crucial for a more comprehensive understanding of the dynamics between social media and website interactions. Incorporating this perspective would not only refine the paper's analysis but also contribute to a more accurate portrayal of the intricate relationship between these two variables.

Moreover, an essential consideration in assessing the suitability of statistical analyses for this study is the potential skewness of metric data. I might be wrong, but given that social media metrics and website traffic data often exhibit skewed distributions (with much variation between different media outlets in terms of the traffic they are likely to receive), the choice of statistical methodology becomes critical. Is this potentially the case with your dataset too? While the paper employs correlation analyses, it raises the question of whether ordinary least squares (OLS) regression is the most appropriate. OLS regression assumes that the residuals are normally distributed, which might not be the case when dealing with highly skewed data. In such instances, alternative regression techniques, such as robust regression or transformations to address skewness, may warrant exploration. Addressing this aspect could enhance the paper's methodological robustness and contribute to a more nuanced understanding of the relationship between social media metrics and website traffic. Additionally, considering again the nature of metric data, it also becomes pertinent to scrutinize the appropriateness of using the mean as a central measure. Metric data often exhibit variability, and the presence of outliers can unduly influence the mean, making it a less robust indicator of the central tendency. Given the potential for extreme values in social media metrics, such as viral posts or anomalies in website traffic, incorporating the median as a measure of central tendency may be imperative. The median is less sensitive to outliers and provides a more resistant estimate in the presence of skewed or asymmetric distributions. Therefore, a thorough exploration of the dataset using both the mean and the median could offer a more comprehensive understanding of the central tendency and help mitigate the impact of extreme values on the overall interpretation of the study's findings.

Next, the paper does not adequately address potential confounding variables that could influence both social media metrics and website traffic. Factors such as the type of outlet, content quality, search engine optimization, and external events might impact both variables independently, introducing potential biases in the reported correlations.

With regards to the discussion (and also the theoretical framework) a critical aspect that requires further exploration is the direct relevance of these findings to the concept of reciprocal journalism. The paper establishes a link between social media interactions and subsequent website traffic, but the essential question remains: how precisely do these observations align with the principles of reciprocal journalism? Reciprocal journalism as a concept for me is a bit more fundamental than merely interacting on implies a dynamic relationship where audience engagement and journalistic content interact in a mutually reinforcing manner. Without a nuanced exploration of the qualitative aspects of this interaction, it becomes challenging to ascertain the depth and authenticity of the reciprocal relationship. A more in-depth investigation into the nature of these interactions would contribute significantly to the paper's overarching argument, providing a clearer picture of the mechanisms at play and the extent to which reciprocity is manifested in the realm of social media and website interactions.

In conclusion, while the paper on "Social Media Metrics as Predictors of Publishers' Websites Traffic" presents a thought-provoking exploration of the correlation between social media metrics and website traffic, several critical concerns merit careful consideration before progressing toward publication. The issues related to causality, the potential existence of a feedback loop, and the choice of statistical measures underscore the need for a more robust methodological approach. Additionally, the paper would benefit from a more explicit examination of how the observed correlations align with the principles of reciprocal journalism. Moreover, a thorough assessment of the qualitative nature of the interaction between social media metrics and website traffic is essential for a comprehensive understanding of the reciprocal dynamics at play. Addressing these concerns will undoubtedly enhance the scholarly rigor and depth of the paper, ensuring its contribution to the evolving discourse on the interplay between social media, journalism, and audience engagement. Consequently, a meticulous revision that addresses these points is crucial before considering the manuscript for publication.

Author Response

Thank you for the comprehensive review. We have tried to resolve all the concerns you raised in your comments.

We appreciate the reviewer's insightful comments and welcome the opportunity to clarify the primary focus of our research. Our intention is to examine the predictive value of social media metrics on publishers’ website traffic, emphasizing that our results reveal a statistical relationship, not causation. In this revised manuscript, we have made explicit efforts to highlight that our study employs regression for prediction rather than causal analysis. We added the following sentence to reinforce this point:

“At this point we should underline that in the case of our research, regression is used to make predictions about the dependent variable based on the observed values of the independent variables without further causal analysis, i.e. whether our independent variable actually affects the dependent variable. In particular, as reported in the literature, multiple regression has two main uses, prediction and causal analysis. In the former, the dependent variables take values from the independent variables, while in the latter, the independent variables are considered causes of the dependent variable (Allison, 1999).”

Allison, P. D. (1999). Multiple regression: A primer. Pine Forge Press.

Acknowledging the potential existence of this feedback loop is crucial for a more comprehensive understanding of the dynamics between social media and website interactions. Incorporating this perspective would not only refine the paper's analysis but also contribute to a more accurate portrayal of the intricate relationship between these two variables.

We appreciate the reviewer's valuable input and have incorporated the suggested perspective into the revised manuscript. The following sentence has been added to highlight the potential bidirectional relationship between social media metrics and website traffic:

“This relationship could therefore work both ways, as increased website traffic can in turn affect social media metrics. The potential existence of this feedback loop may be interesting to study by future targeted research on this topic, which could help to better understand the issue”.

We appreciate the reviewer's concern about the potential skewness of metric data. However, after a careful examination of our dataset, we found that the distribution of our data does not exhibit significant skewness, as indicated by the standard deviations not being extremely high. We believe that the use of ordinary least squares (OLS) regression is appropriate for the analysis of our data, especially considering the number of cases (N=50), which enhances the precision of the estimates obtained. To further clarify this point, we have added the following sentence:

“The method of OLS regression is considered appropriate for the analysis of panel data as the big number of cases (N=50) improves the precision of the estimates we obtain.”

Additionally, considering again the nature of metric data, it also becomes pertinent to scrutinize the appropriateness of using the mean as a central measure. Metric data often exhibit variability, and the presence of outliers can unduly influence the mean, making it a less robust indicator of the central tendency. Given the potential for extreme values in social media metrics, such as viral posts or anomalies in website traffic, incorporating the median as a measure of central tendency may be imperative. The median is less sensitive to outliers and provides a more resistant estimate in the presence of skewed or asymmetric distributions. Therefore, a thorough exploration of the dataset using both the mean and the median could offer a more comprehensive understanding of the central tendency and help mitigate the impact of extreme values on the overall interpretation of the study's findings.

We appreciate the reviewer's suggestion to consider the appropriateness of using the mean as a central measure for metric data. After thorough consideration, we believe that the mean is more suitable for our dataset. The inclusion of standard deviations in our analysis provides a comprehensive understanding of the central tendency, and the relatively low values of the standard deviations indicate that our data do not exhibit strong outliers. Therefore, we decided to maintain the use of the mean as it aligns with our analytical approach and contributes to a more nuanced interpretation of the study's findings.

In regard to control variables, given that our dependent variables are aparted by social media metrics, we control the size / capacity of each news outlet by including to our variables the followership (size of audience = number of followers) as well as the number of posts (capacity = content production = number of posts).

We would also like to add that our sample is not heterogenous. The Online Publishers Association of Greece is aparted by members who must cover some standards. Analytically, among others they must:

Produce original content (and not simply copy third-party content).

Employ at least ten (10) employees with a dependent labor contract of any form and/or partners on the basis of fixed-term independent service contracts.

Be owners and/or managers of at least a website, in the Greek language and its legal operation has completed at least 2 years.

They have demonstrated practical activity in the area of digital publications on the internet.

Their websites have daily traffic comparable to their counterparts places of the already existing members.

To have a turnover of at least 500,000 euros per year.

On top of these, our results were tested for the risk for multicollinearity, which was assessed for each model and was deemed acceptable: no high R2 in excess 0.8, no high pairwise correlations among explanatory variables in excess of 0.8, or other indicators (Gujarati, 2009).

Your suggestion to explore the qualitative aspects of the interaction between social media and website interactions in the context of reciprocal journalism is insightful. We agree that a more in-depth investigation into the nature of these interactions is warranted to better align our findings with the principles of reciprocal journalism. In our future extensions section of the discussion, we have already acknowledged the importance of considering the type of content in social media metrics variables, such as video, text, links, or photos, as they may influence engagement and subsequently affect website traffic. Moreover, to address the need for a nuanced exploration, we have added a paragraph emphasizing the significance of a qualitative lens to unravel the reciprocal relationship, examining the tone and themes of audience comments, exploring how media outlets reciprocate, and scrutinizing the mechanisms and challenges of reciprocal journalism.

“Beyond quantitative correlations, a qualitative lens is crucial for unraveling the reciprocal relationship between social media and website interactions. Reciprocal journalism, at its core, implies a symbiotic connection where audience engagement and journalistic content interact synergistically. Future research should delve into the nature of social media interactions, examining the tone and themes of audience comments. Additionally, it could explore how media outlets reciprocate, fostering bidirectional communication. By scrutinizing the mechanisms and challenges of reciprocal journalism, future research could provide a nuanced understanding of how these interactive practices contribute to the dynamics between media outlets and their audience.”

Thank you for your thoughtful and constructive feedback. We have carefully considered your concerns and made significant revisions to address almost all the issues raised. We believe these revisions significantly contribute to the scholarly rigor of the paper, and we appreciate your guidance throughout this process.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

While the study specifies a three-month time range (March to May 2018), it would be good to clarify the possible implications of this time limit on the findings.

Including brief operational descriptions for each social media statistic would aid comprehension, especially for those unfamiliar with these terminologies.

The approach cites the usage of the Brandwatch platform for gathering social media metrics but provides no specifics on the particular techniques used during data collection. Providing information on the frequency of data collection, any pre-processing stages, or data validation methods will improve methodological openness.

Further clarification on specific findings would be helpful, particularly when it comes to the reciprocal journalism idea and the network gatekeeping notion. It would improve the theoretical discussion to provide more detailed insights into these relationships.

For a more comprehensive understanding of the study's depth, a brief description of any limitations, such as the generalizability of findings or any restrictions on data collecting, would be helpful.

The study's applicability, transparency, and clarity could be enhanced if the improvement concerns were taken into account.

Comments on the Quality of English Language

Minor changes might be made in a few places to improve clarity and flow. Pay close attention to the consistent use of verb tenses, especially when presenting findings. Consider alternate word options for more accuracy or to eliminate potential ambiguity.

Author Response

While the study specifies a three-month time range (March to May 2018), it would be good to clarify the possible implications of this time limit on the findings.

Thank you for raising the concern about the three-month time range specified in our study (March to May 2018). We appreciate your feedback, and to address this issue, we have included a statement in the Limitations section. It now reads:

“One notable limitation of this study is the temporal nature of the data utilized. The dataset employed for our analysis reflects a specific timeframe, and as such, the findings may not fully encapsulate the most recent developments in newsroom practices. We acknowledge that the media landscape is subject to continuous evolution, and the data's age could impact the generalizability of our conclusions to the current media environ-ment. To address this limitation, we emphasize that our study serves as a snapshot of practices during the specific period covered by the dataset.”

Including brief operational descriptions for each social media statistic would aid comprehension, especially for those unfamiliar with these terminologies.

Thank you for your suggestion. In response to your feedback, we have included more detailed descriptions for each social media statistic in the Materials and Methods section, particularly under the '3.3.2. Social media metrics - Independent variables' part.

“The independent variables on social media use included indicators mostly referred to in the relevant literature, namely:

a) page likes (followers) on Facebook and Twitter followers (Hong, 2012; Ju et al. 2014),
b) posts (number) on Facebook page and Tweets (Hong in 2012),
c) likes for Facebook page posts (Tandoc and Vos, 2015, Ksiazek et al., 2014),
d) shares of Facebook page posts and retweets (Lishka et al. 2015; Tandoc and Vos, 2015; Ksiazek et al., 2014) and
e) comments on Facebook page posts and replies on Twitter (Lee et al., 2010; Lishka et al., 2015). We categorize comments into two distinct types: a) "audience comments," referring to those made by the audience under each media post or in response to each media tweet, and b) "owner comments," denoting comments made by the media in response to audience comments on each post or tweet.”

The approach cites the usage of the Brandwatch platform for gathering social media metrics but provides no specifics on the particular techniques used during data collection. Providing information on the frequency of data collection, any pre-processing stages, or data validation methods will improve methodological openness.

Thank you for your valuable suggestion. We appreciate the opportunity to provide more details on our methodological approach. We have included additional information in the Methods section to address your concern:

“Owing to Brandwatch platform’s limitations at the time of the research, liking as a form of Twitter engagement was not tested; in case of Facebook, all reactions were included”

Further clarification on specific findings would be helpful, particularly when it comes to the reciprocal journalism idea and the network gatekeeping notion. It would improve the theoretical discussion to provide more detailed insights into these relationships.

Thank you for highlighting the need for further elucidation on specific findings, particularly concerning the reciprocal journalism idea and network gatekeeping concept. In response to your valuable feedback, we have expanded our theoretical discussion to offer more detailed insights into these relationships. Our study, firmly rooted in Reciprocal Journalism (Wallace, 2017), accentuates the diverse influence of the public, emphasizing active engagement through comments on social media. The observed robust correlation between audience interactions, especially comments, and subsequent impacts on media websites resonates with theories of mutual exchange (Groshek and Tandoc, 2017; Lewis et al., 2013). Simultaneously, grounded in the Gatekeeping and Network Gatekeeping Theory (Barzilai-Nahon, 2009), our research unveils the evolving role of social media users as network gatekeepers. Their active involvement in disseminating content on platforms like Facebook and Twitter not only predicts news impact on social media but significantly influences media websites' traffic indicators. This direct and measurable impact extends from the news source to dissemination, underscoring the profound role of public engagement. Moreover, we acknowledge the call for a more nuanced exploration within the framework of reciprocal journalism, emphasizing the need to delve deeper into the nature of interactions, including tone, sentiment, and thematic elements of audience comments. This enriched examination offers a comprehensive grasp of the reciprocal journalism concept, enhancing the authenticity of the observed relationship between media outlets and their audience. We believe these additional insights contribute valuable depth to our empirical findings.

Here are the relevant parts we added:

“Our study integrates two central theories—Reciprocal Journalism and Gatekeeping—to explore the dynamic relationship between media outlets and their audience. Rooted in Reciprocal Journalism (Wallace, 2017), our research underscores the multifaceted influ-ence of the public, emphasizing active engagement through comments on social media. The study reveals a robust correlation between audience interactions, especially com-ments, and subsequent impacts on media websites, aligning with theories of mutual exchange (Groshek and Tandoc, 2017; Lewis et al., 2013).

In parallel, our study, grounded in the Gatekeeping and Network Gatekeeping Theory (Barzilai-Nahon, 2009), unveils the evolving role of social media users as network gatekeepers. Their active involvement in disseminating content on platforms like Fa-cebook and Twitter not only forecasts news impact on social media but significantly in-fluences media websites' traffic indicators. This direct and measurable impact extends from the news source to dissemination, reflecting the profound role of public engagement.

[…] While our study has established a clear correlation between audience engagement on social media and website traffic, a more nuanced exploration of these relationships within the framework of reciprocal journalism is warranted. Specifically, delving deeper into the nature of interactions, such as the tone, sentiment, and thematic elements of audience comments, can provide additional insights into our observed patterns. Understanding how media outlets reciprocate to audience engagement, fostering bidirectional commu-nication, would contribute to a more comprehensive grasp of the reciprocal journalism concept as demonstrated in our study. This deeper examination sheds light on the mechanisms at play and underscores the authenticity of the reciprocal relationship be-tween media outlets and their audience, offering valuable insights from our empirical findings.”

For a more comprehensive understanding of the study's depth, a brief description of any limitations, such as the generalizability of findings or any restrictions on data collecting, would be helpful.

Thank you for your suggestion. We have enriched the limitation section of our manuscript and now we think that offers a deep understanding of our study's depth. Our limitations include the following categories:

Sample Specificity: The study's sample, while large-scale, is drawn from general content publishers. Exclusive surveys focused on news media organizations might unveil variations or commonalities in a more targeted manner.
Content Analysis Gap: The study, concentrating on the relationship between social media platforms and website traffic, does not delve into content specifics such as popularity or other parameters influencing user interactivity.
Limited Generalizability: Results are based on a survey of Greek online publishers, limiting their generalizability. Similar surveys across different countries could determine the applicability of these results in diverse contexts.
Sample Heterogeneity: While efforts were made to maintain sample homogeneity, the inclusion of media with varying profiles and strategic aims in content production and audience reach could impact the study's findings.
Brandwatch Limitations: Social media metrics data was collected using the Brandwatch platform, restricting the study to the most social media-savvy media due to platform limitations.
Temporal Constraints: The study's dataset reflects a specific timeframe, potentially limiting the generalizability of findings to the current media landscape. The dynamic nature of newsroom practices could have evolved since the dataset's collection.
Dialogical Interaction: The scarcity of publisher comments on Facebook and replies on Twitter highlights the need for further research to explore media efforts in fostering a dialogue with the public.

Here is the relevant part:

Limitations

Our research has some limitations. To conduct a large-scale study, our sample was an extract of general content publishers. More specific surveys covering exclusively news media organizations can shed light on any variations or commonalities. In its effort to investigate the relationship between the use of social media platforms and website traffic, our study did not examine the content, i.e., whether it was popular. Furthermore, it did not analyze the other parameters that lead to different user behaviors in terms of inter-activity (Bobkowski et al., 2018; Al Rawi 2017).

The survey sample includes Greek online publishers; therefore, the results cannot be generalized. Similar surveys in other countries could answer the question regarding these results being applicable in general or due to specific characteristics of the target audience in these countries. Furthermore, our survey sample may well include media with dif-ferent profiles and strategic aims in terms of content production and audience reach. While our sample was not so heterogenous, it did not include only news sites. Hence, for the current research, we were limited to a “first picture” on publishers’ website traffic pre-diction models based on social media metrics.

The dataset of social media metrics was collected by employing the Brandwatch platform. Due to the platform’s limitations, we were unable to study the total group of Greek online publishers’ websites. Hence, we decided to study the most social media–savvy media, supposing that they will give us more useful data.

One notable limitation of this study is the temporal nature of the data utilized. The dataset employed for our analysis reflects a specific timeframe, and as such, the findings may not fully encapsulate the most recent developments in newsroom practices. We acknowledge that the media landscape is subject to continuous evolution, and the data's age could impact the generalizability of our conclusions to the current media environ-ment. To address this limitation, we emphasize that our study serves as a snapshot of practices during the specific period covered by the dataset.

Finally, we found very few publishers (owners') comments on Facebook and replies on Twitter. More research is therefore necessary to find the results of the media efforts to develop a dialogue with the public.

The study's applicability, transparency, and clarity could be enhanced if the improvement concerns were taken into account.

Thank you for your insightful comments. We have carefully considered your suggestions and made significant improvements. We appreciate your valuable input in refining our work.

Based on your prompt we have conducted a comprehensive proofreading of the entire paper.

Author Response File: Author Response.pdf

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