Analyzing microblogs with affinity propagation

Kang, Jeon Hyung; Lerman, Kristina; Plangprasopchok, Anon

doi:10.1145/1964858.1964868

Cited by 16 publications

(9 citation statements)

References 7 publications

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“…As a result, traditional clustering techniques might not achieve high performance when applied to short-texts. Several authors (Kang et al, 2010;Rangrej et al, 2011) compared the performance of clustering algorithms that are traditionally applied to long-texts for the task of short-text clustering. Rangrej et al (2011) evaluated the performance of three traditional algorithms: k-Means, singular value decomposition (SVD) and the graph-based approach affinity propagation (AP).…”

Section: Traditional Clustering Techniquesmentioning

confidence: 99%

“…The performance worsened when tweets were assigned to multiple clusters, which could be caused by the short nature of tweets, and thus the difficulty of identifying multiple overlapping topics. Kang et al (2010) proposed to apply AP for clustering tweets that contained links to news articles based on the supposition that tweets sharing the same link belonged to the same cluster. The authors only considered content-based features extracted from tweets such as: words, hashtags and links, and the Cosine similarity between them.…”

Section: Traditional Clustering Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Short-text learning in social media: a review

Tommasel

Godoy

2019

The Knowledge Engineering Review

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Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

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Section: Traditional Clustering Techniquesmentioning

confidence: 99%

Section: Traditional Clustering Techniquesmentioning

confidence: 99%

Short-text learning in social media: a review

Tommasel

Godoy

2019

The Knowledge Engineering Review

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“…Microblog messages can be divided into three categories according to [22]: broadcast messages, conversation messages, and retweet messages. A broadcast message is published on the wall by some user A without a specific recipient.…”

Section: A Content Based Social Linksmentioning

confidence: 99%

Information propagation in microblog networks

Zhang

Sun

Wang

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Abstract-Information propagation in a microblog network aims to identify a set of seed users for propagating a target message to as many interested users as possible. This problem differs from the traditional influence maximization in two major ways: it has a content-rich target message for propagation and it treats each link in the network as communication on certain topics and emphasizes the topic relevance of such communication in propagating the target message. In realistic situations, however, the topics associated with a link are not explicitly expressed but are hidden in the microblogs previously exchanged through the link. In this paper, we present a topic-aware solution to information propagation in a microblog network. We first model the latent topic structure of the network using observed microblog messages published in the network. We then present two methods for estimating the propagation probability based on the topic relevance between a link and the target message. Once the propagation probability is estimated, we adopt the standard greedy algorithm for influence maximization to find seed users. This approach is topic-aware in that the target message finds its way of propagation according to its topic relevance to the latent topic structure in the network. Experiments conducted on real Twitter datasets suggest that the proposed methods are able to select right seed users.

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“…It aims at grouping samples into several clusters, samples in the same cluster are similar, while samples in different clusters are dis-similar. From practical perspective, clustering has been applied in many areas, such as genomic data analysis [1], image segmentation [2], social network [3], market analysis [4], anomaly detection [5] and so on. However, with the development of information technology, traditional clustering algorithms faced with two challenges: the structure of samples becomes much more complex than before and the amount of samples increases sharply.…”

Section: Introductionmentioning

confidence: 99%

Affinity Propagation Clustering Using Path Based Similarity

Jiang

Liao

2016

Algorithms

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Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. However, since it mainly uses negative Euclidean distance between exemplars and samples as the similarity between them, it is difficult to identify clusters with complex structure. Therefore, the performance of APC deteriorates on samples distributed with complex structure. To mitigate this problem, we propose an improved APC based on a path-based similarity (APC-PS). APC-PS firstly utilizes negative Euclidean distance to find exemplars of clusters. Then, it employs the path-based similarity to measure the similarity between exemplars and samples, and to explore the underlying structure of clusters. Next, it assigns non-exemplar samples to their respective clusters via that similarity. Our empirical study on synthetic and UCI datasets shows that the proposed APC-PS significantly outperforms original APC and other related approaches.

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Analyzing microblogs with affinity propagation

Cited by 16 publications

References 7 publications

Short-text learning in social media: a review

Short-text learning in social media: a review

Information propagation in microblog networks

Affinity Propagation Clustering Using Path Based Similarity

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