Vote-and-Comment: Modeling the Coevolution of User Interactions in Social Voting Web Sites

Costa, Alceu Ferraz; Traina, Agma J. M.; Traina, Caetano; Faloutsos, Christos

doi:10.1109/icdm.2016.0020

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…previous votes). Costa et al (2016) have modeled the coevolution of user interactions in social voting websites to address the question of how the number of up-votes, down-votes and comments evolves over time. Overall, however, there is a lack of analysis of the aggregation of helpfulness votes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some scholars have proposed that helpfulness voting is a type of aggregate user representation (Walther et al, 2012), and that its aggregative process takes time (Zhang and Tran, 2010). There have also been a few in-depth studies about the aggregative process (Costa et al, 2016;Walther et al, 2012). In order to understand the aggregation of helpfulness votes, it is important to explore a review's helpfulness vote from a dynamics perspective, which gives a continuous indication of how the community has validated it as the "representative" view of the readers about a review (L opez-L opez and Parra, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Vote or not? How various information cues affect helpfulness voting of online reviews

2020

OIR

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PurposeThe helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.Design/methodology/approachA dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.FindingsThe results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.Practical implicationsThis paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.Originality/valueThis study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vote or not? How various information cues affect helpfulness voting of online reviews

2020

OIR

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“…For news tweeted by major organisations, time of day can affect the number of retweets as well as the number of people receiving it at an early stage [62]. Time since posting impacts upvotes in Reddit and similar sites [63]. The presence of hashtags or URLs also associates with increased retweeting [56,61].…”

Section: Factors Affecting the Success Of A Postmentioning

confidence: 99%

Can social news websites pay for content and curation? The SteemIt cryptocurrency model

Thelwall

2017

Journal of Information Science

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SteemIt is a Reddit-like social news site that pays members for posting and curating content. It uses micropayments backed by a tradeable currency, exploiting the Bitcoin cryptocurrency generation model to finance content provision in conjunction with advertising. If successful, this paradigm might change the way in which volunteer-based sites operate. This paper investigates 925,092 new members' first posts for insights into what drives financial success in the site. Initial blog posts on average received $0.01, although the maximum accrued was $20,680.83. Longer, more sentiment-rich or more positive comments with personal information received the greatest financial reward in contrast to more informational or topical content. Thus, there is a clear financial value in starting with a friendly introduction rather than immediately attempting to provide useful content, despite the latter being the ultimate site goal. Follow-up posts also tended to be more successful when more personal, suggesting that interpersonal communication rather than quality content provision has driven the site so far. It remains to be seen whether the model of small typical rewards and the possibility that a post might generate substantially more are enough to incentivise long term participation or a greater focus on informational posts in the long term.

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“…comments or votes) which only occur between opposing fractions. In addition, the co-evolution of votes and comments is currently receiving increasing attention [100]. Therefore, we suggest that the research challenge of modeling online discussions including groups of users (to be defined with interactions from the voting layer) will provide a better explanation of the behavior of online communities in discussion platforms.…”

Section: Groups Of Usersmentioning

confidence: 99%

Generative models of online discussion threads: state of the art and research challenges

Aragón

Gómez

García

et al. 2017

J Internet Serv Appl

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Online discussion in form of written comments is a core component of many social media platforms. It has attracted increasing attention from academia, mainly because theories from social sciences can be explored at an unprecedented scale. This interest has led to the development of statistical models which are able to characterize the dynamics of threaded online conversations. In this paper, we review research on statistical modeling of online discussions, in particular, we describe current generative models of the structure and growth of discussion threads. These are parametrized network formation models that are able to generate synthetic discussion threads that reproduce certain features of the real discussions present in different online platforms. We aim to provide a clear overview of the state of the art and to motivate future work in this relevant research field.

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Vote-and-Comment: Modeling the Coevolution of User Interactions in Social Voting Web Sites

Cited by 5 publications

References 24 publications

Vote or not? How various information cues affect helpfulness voting of online reviews

Vote or not? How various information cues affect helpfulness voting of online reviews

Can social news websites pay for content and curation? The SteemIt cryptocurrency model

Generative models of online discussion threads: state of the art and research challenges

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