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This study aims to investigate replies to the top 10 comments under Always “Like a Girl” YouTube femvertising video to gauge consumers’ responses regarding femvertising as well as relationships among commenters.

This study adopted a mixed research methods design. A user analysis and a qualitative content analysis were conducted to examine the replies of the top 10 comments with the most replies to reveal not only the topics but also relationships and patterns among those comments and commenters.

The user analysis found that across all the 10 comment-and-reply units, in 8 units, the user of the original primary comment, the conversation starter, was also the user who was targeted most often. The qualitative content analysis revealed four themes from the 10 comment-and-reply units: multilayered emotional responses, a gendered society, complex coexisting relationships and a melting pot.

The findings of this research offer significant extensions to the understanding of public sphere theory within the contemporary digital media landscape. By analyzing the nature of replies to digital advertisements, the study illuminates how various types of user engagement–whether it be inquiry, laudation, debate, or flame–play a critical role in shaping the digital public sphere.

The study underscores the importance for marketers to scrutinize both comments and replies to effectively utilize femvertising on social media, particularly YouTube. By understanding the emotional dynamics of user interactions, marketers can craft strategies that evoke positive responses and mitigate negative ones. Engaging with users who are open to changing their views or mediating discussions can also be beneficial, as can the use of AI tools to maintain focus on the content rather than on individual commenters. Such approaches can enhance the perception of femvertising campaigns and foster a more constructive dialogue within the social media space.

This study contributes to the literature by investigating the replies of comments, interactions, relationships and patterns among YouTube commenters that may generate valuable insights for advertisers and marketers to be aware of the possible issues and monitor the sentiment of commentaries, thus, developing effective strategies to better connect with consumers. This extends the understanding of public sphere theory in the contemporary digital media landscape.

The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web.

The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations.

Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments.

Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.

This study aims to reveal the effect of comment interactions on video engagement of users in video-sharing platforms.

The authors collected 87,232 comments on 647 videos of Korean beauty creators on YouTube and conducted a social network analysis and a hierarchical regression analysis.

The results present that the more evenly interactive participants write and receive replies in the comments section, the more users' video engagement increases. The more creators reply to user comments and the more reactions they present, the more video engagement increases. Additionally, the influence of the creator's interaction on user engagement increased as the number of commenting participants decreased.

This study has implications for platform operators regarding comment section design and proposes interaction strategies for content creators to induce users' video engagement.

Compared to previous studies, this study empirically verifies the influence of interactions on video-sharing platforms in detail by confirming the influence of user interaction structures and creator's participation in the interaction on video engagement.

This study aimed to empirically examine the effectiveness of disclosing user comment history without disclosing personal identity as a nudge policy to refrain users from posting malicious content online.

The authors collected the number of comments and posters from the leading portal website in South Korea, Naver.com. To causally investigate the impacts of the new nudge policy on the number of comments and posters, the authors used the regression discontinuity design (RDD) approach.

The authors found that the new policy reduced all types of comments, including the number of malicious comments, self-deleted comments and current comments. This resulted in an overall decrease in the total number of posted comments, which is considered a side effect. In addition, the authors found that the effect of the nudge policy, which disclosed user comment history, has a stronger effect on older female users than their counterparts.

The study findings extend the current knowledge on a nudge policy being implemented by a website as a means to reduce malicious online content and how it impacts user content posting behaviors.

The purpose of this paper is to analyze user-generated comments posted on social media while live matches were being played during the Cricket World Cup 2015.

Data were collected from Yahoo! Cricket (YC), a website that allows people to submit comments during live matches. The comments were qualitatively analyzed using the grounded theory approach.

The key finding of this paper is that people like to consume live sporting events in an online social setting rather than as isolated individuals. In addition, the use of the grounded theory approach helped uncover several new findings related to people’s use of social media during live matches.

Since this paper studied the case of the Cricket World Cup 2015 and collected data from YC, caution is advocated in generalizing its findings.

Scholarly interest on the use of social media during live sporting events is growing. Building on such works, this paper highlights how user-generated comments posted during the Cricket World Cup 2015 – mostly by individuals within the Indian subcontinent – intersected with broader issues such as culture, identity, politics and religion.

The video-sharing website YouTube encourages interaction between its users via the provision of a user comments facility. This was originally envisaged as a way for viewers to provide information about and reactions to videos, but is employed for other communicative purposes including sharing ideas, paying tributes, social networking, and question answering. This study seeks to examine and categorise the types of comments created by YouTube users to highlight the various ways in which this interactive feature has been employed as a means of communication and self-expression.

By conducting a content analysis of 66,637 user comments on YouTube videos the authors created a classification schema which may be used to categorise the types of comments users leave.

The schema reveals ten broad categories, and 58 subcategories which reflect the wide-ranging use of the YouTube comments facility.

As YouTube continues to evolve, new types of comments that do not appear in the scheme outlined will appear. However, this schema will provide an initial structure upon which other investigations can build when analysing the ongoing use of the YouTube comments feature as a communication device.

This scheme may be used for researchers in a variety of disciplines who are interested in using user-generated content. The scheme will aid in the description and mining of this content and provides a way of structuring this content into categories representing user intent.

This study highlights the variety of purposes to which the user commenting facility of YouTube is employed. These include purposes such as reminiscence, grieving, giving advice and communication.

This is the first detailed, content-based analysis of the types of comments created by YouTube users. The classification scheme facilitates the analysis of these comments for a variety of purposes, including marketing, communication studies and studies of information seeking.

Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour.

More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis.

The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour.

This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.

The purpose of this paper is to investigate the impact of different ways of message framing on users’ engagement behavior regarding the brand posts on Facebook and to determine whether users’ thumbs-up and reply moderate this impact.

A panel data analysis was conducted on a panel with 11,894 observations on 850 unique brand posts from the Facebook pages of the world’s most valuable brands over a seven days window with two observations each day. A system of equations was estimated using ordinary least squares, Hausman–Taylor IV and seemingly unrelated regressions to test study’s hypotheses.

The empirical findings confirm that more positively and negatively framed comments result in increased users’ engagement. Also, an increase in thumbs-up ratio for neutrally and negatively framed comments results in less engagement. The reply ratio might also have a positive and negative moderation effect on the influence of neutrally and positively framed comments on engagement behavior, respectively.

This study provides an in-depth understanding of online social interactions on Facebook pages for firms’ managers and marketers. Online social interactions might be either harmful or fruitful for firms depending on the type of interaction and engagement behavior. Findings can help managers and marketer to improve their strategies for leveraging Facebook for electronic marketing.

This is likely to be the first study that examines the moderating effect of users’ thumbs-up and reply on the relationship between message framing and users’ engagement behavior. By providing robust findings by addressing issues like omitted variables and endogeneity, the findings of this study are promising for developing new hypotheses and theoretical models in the context of online social interactions.

The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback.

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.

Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.

The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users.

The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.

The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community forums.

The authors are aiming to build a classifier to distinguish trolls vs regular users. Unfortunately, it is not easy to get reliable training data. The authors solve this issue pragmatically: the authors assume that a user who is called a troll by several people is likely to be such, which are called accused trolls. Based on this assumption and on leaked reports about actual paid opinion manipulation trolls, the authors build a classifier to distinguish trolls vs regular users.

The authors compare the profiles of paid trolls vs accused trolls vs non-trolls, and show that a classifier trained to distinguish accused trolls from non-trolls does quite well also at telling apart paid trolls from non-trolls.

The troll detection works even for users with about 10 comments, but it achieves the best performance for users with a sizable number of comments in the forum, e.g. 100 or more. Yet, there is not such a limitation for troll comment detection.

The approach would help forum moderators in their work, by pointing them to the most suspicious users and comments. It would be also useful to investigative journalists who want to find paid opinion manipulation trolls.

The authors can offer a better experience to online users by filtering out opinion manipulation trolls and their comments.

The authors propose a novel approach for finding paid opinion manipulation trolls and their posts.