Identifying interesting Twitter contents using topical analysis

Yang, Min-Chul; Rim, Hae-Chang

doi:10.1016/j.eswa.2013.12.051

Cited by 90 publications

(55 citation statements)

References 13 publications

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“…(6). Therefore, our proposed concept introduces a novel IS that is measured by three effective factors, namely re-tweeting strength, commencing intensity and mentioning density.…”

Section: Discussionmentioning

confidence: 99%

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Identifying Influential People Based on Interaction Strength

Zia¹,

Zhang²,

Chen³

et al. 2017

J Inf Process Syst

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Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

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“…(6). Therefore, our proposed concept introduces a novel IS that is measured by three effective factors, namely re-tweeting strength, commencing intensity and mentioning density.…”

Section: Discussionmentioning

confidence: 99%

“…Other leading social networks include instant messaging networks [3], mobile social networks [4] and scientific collaboration networks [5]. Twitter [6] and Sina Weibo [7] are among the most popular blogging service platforms that have been studied for last several years. Under such information sharing…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential People Based on Interaction Strength

Zia¹,

Zhang²,

Chen³

et al. 2017

J Inf Process Syst

View full text Add to dashboard Cite

show abstract

“…Investigation of such extension and issues that may arise, such as how to regulate its behavior according to pre-decided rationale, may lead to a more practical filter. In addition, incorporating unsupervised scheme, such as the Yang and Rim (2014) approach, could lead to a more robust filter.…”

Section: Experimental Analysismentioning

confidence: 99%

“…Additionally, Yang and Rim (2014) defined interesting as possibly being "of potential interest to not only the authors and their followers but a wider audience" and uninteresting as being "only interesting to the authors and their friends due to personal interests." However, they associated "general and mundane topics appear any time spans" to uninteresting posts.…”

Section: Introductionmentioning

confidence: 99%

“…Later, Yang and Rim (2014) took a topic analysis approach based on a Latent Dirichlet allocation (LDA) topic model, as described by Blei et al (2003) to develop an automatic system to assess post interestingness. Additionally, Yang and Rim (2014) defined interesting as possibly being "of potential interest to not only the authors and their followers but a wider audience" and uninteresting as being "only interesting to the authors and their friends due to personal interests."…”

Section: Introductionmentioning

confidence: 99%

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A Social Network Newsworthiness Filter Based on Topic Analysis

Noyunsan¹,

Katanyukul²,

Wu³

et al. 2016

IJTech

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Assessing trustworthiness of social media posts is increasingly important, as the number of online users and activities grows. Current deploying assessment systems measure post trustworthiness as credibility. However, they measure the credibility of all posts, indiscriminately. The credibility concept was intended for news types of posts. Labeling other types of posts with credibility scores may confuse the users. Previous notable works envisioned filtering out non-newsworthy posts before credibility assessment as a key factor towards a more efficient credibility system. Thus, we propose to implement a topic-based supervised learning approach that uses Term Frequency-Interim Document Frequency (TF-IDF) and cosine similarity for filtering out the posts that do not need credibility assessment. Our experimental results show that about 70% of the proposed filtering suggestions are agreed by the users. Such results support the notion of newsworthiness, introduced in the pioneering work of credibility assessment. The topic-based supervised learning approach is shown to provide a viable social network filter.

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A survey on the techniques, applications, and performance of short text semantic similarity

Han

Zhang

Yuan

et al. 2020

Concurrency and Computation

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Summary Short text similarity plays an important role in natural language processing (NLP). It has been applied in many fields. Due to the lack of sufficient context in the short text, it is difficult to measure the similarity. The use of semantics similarity to calculate textual similarity has attracted the attention of academia and industry and achieved better results. In this survey, we have conducted a comprehensive and systematic analysis of semantic similarity. We first propose three categories of semantic similarity: corpus‐based, knowledge‐based, and deep learning (DL)‐based. We analyze the pros and cons of representative and novel algorithms in each category. Our analysis also includes the applications of these similarity measurement methods in other areas of NLP. We then evaluate state‐of‐the‐art DL methods on four common datasets, which proved that DL‐based can better solve the challenges of the short text similarity, such as sparsity and complexity. Especially, bidirectional encoder representations from transformer model can fully employ scarce information of short texts and semantic information and obtain higher accuracy and F1 value. We finally put forward some future directions.

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Identifying interesting Twitter contents using topical analysis

Cited by 90 publications

References 13 publications

Identifying Influential People Based on Interaction Strength

Identifying Influential People Based on Interaction Strength

A Social Network Newsworthiness Filter Based on Topic Analysis

A survey on the techniques, applications, and performance of short text semantic similarity

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