Event Detection and User Interest Discovering in Social Media Data Streams

Shi, Leilei; Liu, Lu; Wu, Yan; Jiang, Liang; Hardy, Jennifer

doi:10.1109/access.2017.2675839

Cited by 67 publications

(57 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ART works with similar tweets on users' interest, hashtags, users' recipients, which are united in a single document. Shi et al, [72] construct a user-interest model by merging the short corpus to lengthy corpus and finds the topic relations based on each user events and their similarity. Using a baseline of LDA and Author-Topic Model [23], Tsai [58] also worked with inclined tags to extract similar topics from blogs.…”

Section: Topic Modeling On Short Textsmentioning

confidence: 99%

“…Chen et al, [71] proposed one effective method, KGNF (Knowledge-guided Non-negative matrix factorization) which is designed with word-word pair wise semantic regularization via low-rank representations with the time-efficient algorithm. Shi et al, [72] developed Semantics-assisted Non-negative Matrix Factorization (SeaNMF) model to integrate semantic relations between word and context. Skip-gram model leverages to learn the context relations from the corpus.…”

Section: Topic Modeling On Short Textsmentioning

confidence: 99%

“…[15], [19], [22], [23], [27] , [32], [35], [44][45][46], [51], [60][66], [67], [72], [73], [75], [80], [82], [83] Open Directory Project (ODP) - [37] Tweets related to Apple, Google, Microsoft, Twitter - [36], [70] Weibo Collections - [15], [35] Amazon Reviews (May 1994 -June 2014 contains product reviews) -7 [41] [45]…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Detailed Survey on Topic Modeling for Document and Short Text Data

Likhitha¹,

Junior²,

H.³

2019

IJCA

View full text Add to dashboard Cite

show abstract

Section: Topic Modeling On Short Textsmentioning

confidence: 99%

Section: Topic Modeling On Short Textsmentioning

confidence: 99%

See 1 more Smart Citation

A Detailed Survey on Topic Modeling for Document and Short Text Data

Likhitha¹,

Junior²,

H.³

2019

IJCA

View full text Add to dashboard Cite

show abstract

“…Cyber-enabled online services [1], [2], [3], [4], [5], [6], [7] have become an integral part of daily activities. Microblogging is one of the popular cyber-enabled online services which aid sharing and disseminating information any time and anywhere [8], [9], [10], [11]. Being an information sharing platform [9], [12], [13], [14], microblogging also attracts many users on social media to establish friendships [15], exchange ideas, and to promote products.…”

Section: Introductionmentioning

confidence: 99%

“…Microblogging is one of the popular cyber-enabled online services which aid sharing and disseminating information any time and anywhere [8], [9], [10], [11]. Being an information sharing platform [9], [12], [13], [14], microblogging also attracts many users on social media to establish friendships [15], exchange ideas, and to promote products. As a consequence, such activities in microblogging generate an enormous amount of data [16] with rich semantic content and structure.…”

Section: Introductionmentioning

confidence: 99%

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

Shi

Liu

et al. 2019

IEEE Trans. Comput. Soc. Syst.

Self Cite

View full text Add to dashboard Cite

Microblogging, networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carries opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a Human-centric Social Computing (HCSC) model for hot event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are pre-processed through Hypertext Induced Topic Search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a Latent Dirichlet Allocation (LDA) based multi-prototype user topic detection method is used for identifying users with high influence in the network. Further, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot event detection and information propagation.

show abstract

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Mumin

Shi

Liu

2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

There has been much interest in link prediction research with significant studies on how to predict missing links or future links in a network based on observed information. However, the key solution to tackle the link prediction problem is how to measure the similarity between the nodes in a network with higher accuracy. Several techniques have been proposed that utilize the similarity between nodes to estimate their proximity in the network. In this paper, an efficient link prediction algorithm that predicts relationships between links using the network structure is proposed. This algorithm uses common neighbors in addition to the degree distribution of the nodes to estimate the possibility of the presence of a link between two nodes in a network based on local information. Extensive experiments are carried out and compared with 12 standard similarity-based methods using seven real-world datasets. The experimental results show that our method has higher prediction accuracy compared with most of the local information based methods like the Common Neighbor and Preferential Attachment.It is also competitive with the quasi-local indicators such as LP and global indicators like Katz, with a lower computational complexity than the two.

show abstract

Event Detection and User Interest Discovering in Social Media Data Streams

Cited by 67 publications

References 20 publications

A Detailed Survey on Topic Modeling for Document and Short Text Data

A Detailed Survey on Topic Modeling for Document and Short Text Data

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Contact Info

Product

Resources

About