Sina Weibo Bursty Event Detection Method

Chen, Danny; Li, Donghao; Liang, Fan

doi:10.1109/access.2019.2951926

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…It is also observed that the temporal relationships between terms are no featured; hence, event detection methods lose an important feature. There have been models that seek to overcome these problems [ 17 , 18 ]. In general, there have been issues of accuracy and efficiency in event detection [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Malik

Aslam

et al. 2022

Computational Intelligence and Neuroscience

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People’s lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people’s interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

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Section: Related Workmentioning

confidence: 99%

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Malik

Aslam

et al. 2022

Computational Intelligence and Neuroscience

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“…Firms create social media accounts to post firm-generated content (FGC) for brand publicity. Visual content or images play a critical role in FGC because as the old saying goes, “a picture is worth a thousand words.” More than 120 million images are posted daily on Sina Weibo, a major microblogging platform in China, and 300 million images are uploaded to Facebook (Smith, 2013; Yang, 2019). Visual content has the ability to capture consumers’ attention, provoke emotional reactions, and increase ad persuasiveness, indicating its superiority over textual content in eliciting consumers’ emotions and recall (Bashirzadeh et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Differential effects of visual complexity in firm-generated content on consumer engagements: a deep learning approach

Wang,

Yue,

Yuan

et al. 2024

MIP

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PurposeThis research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.Design/methodology/approachDrawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.FindingsThe results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.Originality/valueAlthough images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

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