A straw shows which way the wind blows

Yin, Peifeng; Luo, Ping; Wang, Min; Lee, Wang-Chien

doi:10.1145/2124295.2124370

Cited by 23 publications

(4 citation statements)

References 18 publications

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“…Ability to predict popularity of a topic is beneficial in various ways. Companies can formulate more effective marketing strategies, for example by using predicted popularity to build recommender systems and provide targeted advertising as described in [11], [15], and [16]. In addition, web services can achieve higher efficiency and provide a better user experience based on prediction.…”

Section: Chapter 2: Popularity Predictionmentioning

confidence: 99%

“…In addition, the comments of popular users in a particular subreddit could be used to determine if a post is likely to be popular. This research would expand on the study in [11] where the researchers looked at the role of "mavericks" and "conformers" in swaying the popularity of online content. For example,…”

Section: Further Researchmentioning

confidence: 99%

“…Posts that prove to be extremely popular can be displayed on Reddit's front page, which garners even more attention, but most posts do not receive such attention [4], [11].…”

Section: Introductionmentioning

confidence: 99%

“…Submissions that achieve front-page status are more available to users who may have not been readers of the original subreddit that the post came from, as mentioned in [11].…”

Section: Introductionmentioning

confidence: 99%

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Popularity Prediction of Reddit Texts

Rohlin¹

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Popularity prediction is a useful technique for marketers to anticipate the success of marketing campaigns, to build recommendation systems that suggest new products to consumers, and to develop targeted advertising. Researchers likewise use popularity prediction to measure how popularity changes within a community or within a given timespan. In this paper, I explore ways to predict popularity of posts in reddit.com, which is a blend of news aggregator and community forum. I frame popularity prediction as a text classification problem and attempt to solve it by first identifying topics in the text and then classifying whether the topics identified are more characteristic of popular or unpopular texts. This classifier is then used to label unseen texts as popular or not dependent on the topics found in these new posts. I explore the use of Latent Dirichlet Allocation and term frequency-inverse document frequency for topic identification and naïve Bayes classifiers and support vector machines for classification. The relation between topics and popularity is dynamic-topics in Reddit communities can wax and wane in popularity. Despite the inherent variability, the methods explored in the paper are effective, showing prediction accuracy between 60% and 75%. The study contributes to the field in various ways. For example, it provides novel data for research and development, not only for text classification but also for the study of relation between topics and popularity in general. The study also helps us better understand different topic identification and classification methods by illustrating their effectiveness on real-life data from a fast-changing and multipurpose website. v

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Section: Chapter 2: Popularity Predictionmentioning

confidence: 99%

Section: Further Researchmentioning

confidence: 99%

“…Posts that prove to be extremely popular can be displayed on Reddit's front page, which garners even more attention, but most posts do not receive such attention [4], [11].…”

Section: Introductionmentioning

confidence: 99%

“…Submissions that achieve front-page status are more available to users who may have not been readers of the original subreddit that the post came from, as mentioned in [11].…”

Section: Introductionmentioning

confidence: 99%

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Popularity Prediction of Reddit Texts

Rohlin¹

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Predicting the popularity of topics based on user sentiment in microblogging websites

Wang

Ding

et al. 2017

J Intell Inf Syst

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Behavioral economics show us that emotions play an important role in individual behavior and decision-making. Does this also affect collective decision making in a community? Here we investigate whether the community sentiment energy of a topic is related to the spreading popularity of the topic. To compute the community sentiment energy of a topic, we first analyze the sentiment of a user on the key phrases of the topic based on the recent tweets of the user. Then we compute the total sentiment energy of all users in the community on the topic based on the Markov Random Field (MRF) model and graph entropy model. Experiments on two communities find the linear correlation between the community sentiment energy and the real spreading popularity of topics. Based on the finding, we proposed two models to predict the popularity of topics. Experimental results show the effectiveness of the two models and the helpful of sentiment in predicting the popularity of topics. Experiments also show that community sentiment affects collective decision making of spreading a topic or not in the community.

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