An empirical analysis of characteristics of useful comments in social media

Momeni, Elaheh; Sageder, Gerhard

doi:10.1145/2464464.2464490

Cited by 5 publications

(5 citation statements)

References 1 publication

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“…Following previous work (Momeni and Sageder 2013), here we describe the creation of the learning-based "usefulness" classifier, evaluate it on the manually coded comments, and analyze the impact of individual features for identifying useful comments.…”

Section: Usefulness Classifiermentioning

confidence: 99%

“…In spite of these complexities, methods for estimating the usefulness of user-generated comments are gaining increasing attention (Siersdorfer et al 2010;Diakopoulos, De Choudhury, and Naaman 2012;Momeni and Sageder 2013). The most common approach simply allows all users to vote on (and possibly moderate) the contributions of others (Siersdorfer et al 2010;Hsu, Khabiri, and Caverlee 2009;Lampe and Resnick 2004), thus avoiding an explicit definition of "useful".…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1: Top 5 Features for Useful(U), Not Useful(N) comments; Flickr(top), YouTube(bottom)(Momeni and Sageder 2013) …”

mentioning

confidence: 99%

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Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects

Momeni

Cardie

Ott

2021

ICWSM

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User-generated comments in online social media have recently been gaining increasing attention as a viable source of general-purpose descriptive annotations for digital objects like photos or videos. Because users have different levels of expertise, however, the quality of their comments can vary from very useful to entirely useless. Our aim is to provide automated support for the curation of useful user-generated comments from public collections of digital objects. After constructing a crowd-sourced gold standard of useful and not useful comments, we use standard machine learning methods to develop a usefulness classifier, exploring the impact of surface-level, syntactic, semantic, and topic-based features in addition to extra-linguistic attributes of the author and his or her social media activity. We then adapt an existing model of prevalence detection that uses the learned classifier to investigate patterns in the commenting culture of two popular social media platforms. We find that the prevalence of useful comments is platform-specific and is further influenced by the entity type of the media object being commented on (person, place, event), its time period (e.g., year of an event), and the degree of polarization among commenters.

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Section: Usefulness Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects

Momeni

Cardie

Ott

2021

ICWSM

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“…Examples are the number of tips posted at the venue, the number of likes received by those tips , the numbers of check ins and unique visitors, and the venue category. Tip's Content Features: this set contains features, proposed in [4,5,16,14], related to the structure, syntactics, readability and sentiment of the tip's content. It includes the numbers of characters, words, and URLs or e-mail addresses in the tip, readability metrics and features based on the Part-Of-Speech tags such as percentages of nouns, and adjectives.…”

Section: Popularity Prediction Modelsmentioning

confidence: 99%

What makes your opinion popular?

Vasconcelos

Almeida

Gonçalves

2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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Location based social networks (LBSNs), such as Foursquare, allow users to post micro-reviews, or tips, about the visited places (venues) and to "like" previously posted reviews. Tips may help attracting future visitors, besides providing valuable feedback to business owners. In particular, the number of "likes" a tip receives ultimately reflects its popularity or helpfulness among users. In that context, accurate predictions of which tips will attract more attention (i.e., "likes") can drive the design of automatic tip filtering and recommendation schemes and the creation of more effective marketing strategies. Previous efforts to automatically assess the helpfulness of online reviews targeted mainly more verbose and formally structured reviews often exploiting textual features. However, tips are typically much shorter and contain more informal content. Thus, we here propose and evaluate new regression and classification based models to predict the future popularity of tips. Our experimental evaluation shows that a linear regression strategy, using features of both the user who posted the tip and the venue where it was posted as predictors, produces results that are at least as good as those produced by more sophisticated approaches and/or by using each set of features in isolation.

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“…Previous research [15,46] observed that users tend to seek related information while or after information behavior such as watching an informative video. Users in various services leave comments including opinions [17,31], discussion [50], additional links [28], and so on. The first step in satisfying these information needs is to find and read the comments on Web services.…”

Section: Introductionmentioning

confidence: 99%

Finding Informative Comments for Video Viewing

Choi

Segev

2019

SN COMPUT. SCI.

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Of all the information-sharing methods on the Web, video is a factor with increasing importance and will continue to influence the future Web environment. Various services such as YouTube, Vimeo, and Liveleak are information-sharing platforms that support uploading UGC (user-generated content) to the Web. Users tend to seek related information while or after watching an informative video when they are using these Web services. In this situation, the best way of satisfying information needs of this kind is to find and read the comments on Web services. However, existing services only support sorting by recentness (newest one) or rating (high LIKES score). Consequently, the search for related information is limited unless the users read all the comments. Therefore, we suggest a novel method to find informative comments by considering original content and its relevance. We developed a set of methods composed of measuring informativeness priority, which we define as the level of information provided by online users, classifying the intention of the information posted online, and clustering to eliminate duplicate themes. The first method of measuring informativeness priority calculates the extent to which the comments cover all the topics in the original contents. After the informativeness priority calculation, the second method classifies the intention of information posted in comments. Then, the next method picks the most informative comments by applying clustering methods to eliminate duplicate themes using rules. Experiments based on 20 sampled videos with 1000 comments and analysis of 1861 TED talk videos and 380,619 comments show that the suggested methods can find more informative comments compared to existing methods such as sorting by high LIKES score.

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An empirical analysis of characteristics of useful comments in social media

Cited by 5 publications

References 1 publication

Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects

Properties, Prediction, and Prevalence of Useful User-Generated Comments for Descriptive Annotation of Social Media Objects

What makes your opinion popular?

Finding Informative Comments for Video Viewing

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