Personalized emerging topic detection based on a term aging model

Cataldi, Mario; Di, Luigi; Schifanella, Claudio

doi:10.1145/2542182.2542189

Cited by 26 publications

(13 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Related to the relation between the user's interests and the trending topics, Cataldi et al proposed a reranking approach that makes use of the users' activity and the posts' contents to personalize the emerging topics [27]. This is done by monitoring the usage of the keywords appearing in the tweets over time, and comparing its importance with the user's context, in order to highlight the most emerging topics within the user's interests.…”

Section: Trending Topics In Social Networkmentioning

confidence: 99%

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Khater¹,

Gračanin

Elmongui

2017

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near realtime. As a result of the growth of social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Thus, finding the "matching" users and content is one of the key challenges for social networks sites.Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in.The users' preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. Accordingly, our approach takes into account both personal interests and local communities. We first propose a new dynamic recommendation system model that provides better customized content to the user.That is, the model provides the user with the most important tweets according to his individual interests. We then analyze how changes in the surrounding environment can affect the user's experience. Specifically, we study how changes in the geographical community preferences can affect the individual user's interests. These community preferences are generally reflected in the localized trending topics. Consequently, we present TrendFusion, an innovative model that analyzes the trends propagation, predicts the localized diffusion of trends in social networks and recommends the most interesting trends to the user. Our performance evaluation demonstrate the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFusion accurately predicts places in which a trend will appear, with 98% recall and 80% precision.To my great parents.To my wonderful husband, Ahmad.To my lovely kids, Ali and Abdullah.iii Acknowledgments First and foremost, I thank Allah for the numerous blessings He has bestowed upon me throughout my dissertation journey.

show abstract

Section: Trending Topics In Social Networkmentioning

confidence: 99%

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Khater¹,

Gračanin

Elmongui

2017

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

show abstract

“…Glänzel and Thijs (2012) presented the use of core documents to discover emerging topics by analyzing the cross-citations between core documents and clusters of disciplines in different periods. Cataldi et al (2013) utilized a novel term aging model to compute the burstiness of each term on Twitter to enable users to search for the emerging topics they are interested in. Chen (2013) found that latent domain knowledge was usually highly relevant but received lower citation than mainstream domain knowledge.…”

Section: Related Researchmentioning

confidence: 99%

Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes

Luo

Lin

et al. 2015

Scientometrics

View full text Add to dashboard Cite

Novelty and salience of the research topics are vital for the competitiveness of research institutions and the development of science and technology. In this study, two novel weighting methods were proposed to differentiate the emergence and salience of research topics. A methodology was constructed to measure and visualize the contributions of research institutions to emerging themes and salient ones. The methods were illustrated with the data of ninety Chinese and American Library and Information Science research institutions collected from the Engineering Compendex and China National Knowledge Infrastructure databases between 2001 and 2012. The contributions of the investigated research institutions to the emerging themes and salient ones were calculated and visualized with the Treemap technique. The institutions were further ranked by their contributions and categorized into four types. The findings can help research institutions evaluate novelty and salience of their research topics, discover research fronts and hotspots and promote their research development.

show abstract

“…Problems related to clustering in social media platforms include the identification of topics or memes [33,47,53], and event detection in social streams [8,11,32,34,49]. Meme and topic identification techniques usually emphasize the terms and keywords as signatures of the content.…”

Section: Related Workmentioning

confidence: 99%

Clustering memes in social media streams

JafariAsbagh

Ferrara

Varol

et al. 2014

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics. Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

show abstract

Personalized emerging topic detection based on a term aging model

Cited by 26 publications

References 60 publications

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes

Clustering memes in social media streams

Contact Info

Product

Resources

About