Event analytics via social media

Hu, Yuheng; John, Ajita; Seligmann, Dorée Duncan

doi:10.1145/2072627.2072638

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…Social media reactions to traditional news media can vary not only in volume but also qualitatively. Hu et al [14] record tweets during the broadcast of a speech of the US President. They observe that many tweets refer to the speech in general, except for certain topics which are discussed in more detail.…”

Section: Related Workmentioning

confidence: 99%

Characterizing the life cycle of online news stories using social media reactions

Castillo

El-Haddad

Stempeck

2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

154

121

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This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately.We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.

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Section: Related Workmentioning

confidence: 99%

Characterizing the life cycle of online news stories using social media reactions

Castillo

El-Haddad

Stempeck

2014

Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

154

121

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“…The results from the reviewed papers and articles show different SMA methods and tools. Most of the reviewed articles demonstrate the usage of sentiment analysis in different areas 54.54% of the articles reviewed uses sentiment analysis Su and Chen (2016), Vorvoreanu et al (2013), Chang et al (2017), Xiang et al (2016), Park et al (2016, He et al (2017), Stieglitz (2012, Anyanwu (2019), Shang et al (2018), Dong et al (2013,), Xu et al (2019), Dahal et al (2019), Kannan et al (2018), Martinez et al(2019), Alamsyah (2017), Barrelet et al (2016), Chen (2016), Chumwatana and Wongkolkitsilp (2019) Kannan et al (2018), Hu et al (2011), and Sachdeva and Mc Caffrey (2018, clustering technique 6.81% Jansen et al (2018), Myaeng et al (2016), and Ghosh et al (2017 , natural language processing 6.81% Barrelet et al (2016), Al Kubaizi et al (2015), and Saravan and Perepu ( 2019), text analysis 4.54% Dias et al (2018), andSingh et al (2018), event detection tool 2.27% Weiler (2013) and social network analysis 6.81% Alamsyah (2017), Udanor et al (2016), and Rahmani et al (2013).…”

Section: Sma Methods and Tools Usedmentioning

confidence: 99%

“…Online media SMA has been performed by Jansen et al (2018) by using online Youtube data to group customers into different segments (age, geographical locations) Youtube analytics platform has been used to create customer segments from online news media. SMA application in media industry also includes the study conducted by Hu et al (2011), in this study the authors analyse the response of the twitter users on the public events. The analytical method has been applied to the comments from the public speech to provide deeper understanding of the individual feedback regarding public events on twitter.…”

Section: Summary Of Reviewed Articlesmentioning

confidence: 99%

“…The results show that there is interaction between retailer and their customers but still the interaction is low. SMA application in online media comprise 6.81%; Jansen et al (2018), Hu et al (2011), Araya et al (2017. In politics 6.81% articles has been reviewed; Stieglitz (2012), Udanor et al (2016), andSantander et al (2020).…”

Section: The Areas Which Sma Has Been Donementioning

confidence: 99%

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A Review of Usage and Applications of Social Media Analytics

Madila¹,

Dida²,

Kaijage³

2021

J INFORM SYSTEMS ENG

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This paper presents the report of a social media analytics (SMA) review. The review conducted to find out the methods and tools used in social media analytics, types of social media platforms which the SMA are performed and the field which SMA has been performed. Social media contains a lot of user uploaded data in different formats like text, images, photos, video etc. These large volumes of data are converted in meaningful information which can be understood using different methods and tools which are called social media analytics. A literature review of articles published between 2010-2020 has been conducted using articles obtained from reputable databases IEEE Xplore, ACM digital, Emerald insight, Springer Link and Science direct. A number of 44 articles have been selected for review from 110 retrieved papers. The paper has been reviewed according to the study objectives. The study found that SMA tools and techniques which have been used are sentiment analysis, youtube analytics, visible intelligence, IBM Watson tool and predictive models. The social media platforms which were mostly used are twitter, facebook, youtube, trip advisor and blogs. SMA has been observed in different fields like agriculture, politics, health, social and business sector.

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“…The latent dirichlet allocation (LDA) algorithm is a representative topic modeling approach [1] where a set of documents is grouped into latent topics with a distinct Dirichlet distribution and each topic is described as a Dirichlet distribution of occurring terms in the document set. The LDA algorithm has been applied to various domains, such as topic extraction for the abstracts of a research paper set [2], analysis of news articles to interpret relevant social situations [3,4], and identification of consumer characteristics and market trends from social network service (SNS) data [5]. However, the traditional LDA algorithm uses the frequency of terms in text documents as a basis to extract their latent topics.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

Park

Kim

et al. 2019

Applied Sciences

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Latent dirichlet allocation (LDA) is a representative topic model to extract keywords related to latent topics embedded in a document set. Despite its effectiveness in finding underlying topics in documents, the traditional algorithm of LDA does not have a process to reflect sentimental meanings in text for topic extraction. Focusing on this issue, this study aims to investigate the usability of both LDA and sentiment analysis (SA) algorithms based on the affective level of text. This study defines the affective level of a given set of paragraphs and attempts to analyze the perceived trust of the methodologies in regards to usability. In our experiments, the text of the college scholastic ability test was selected as the set of evaluation paragraphs, and the affective level of the paragraphs was manipulated into three levels (low, medium, and high) as an independent variable. The LDA algorithm was used to extract the keywords of the paragraph, while SA was used to identify the positive or negative mood of the extracted subject word. In addition, the perceived trust score of the algorithm was evaluated by the subjects, and this study verifies whether there is a difference in the score according to the affective levels of the paragraphs. The results show that paragraphs with low affect lead to the high perceived trust of LDA from the participants. However, the perceived trust of SA does not show a statistically significant difference between the affect levels. The findings from this study indicate that LDA is more effective to find topics in text that mainly contains objective information.

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Event analytics via social media

Cited by 7 publications

References 5 publications

Characterizing the life cycle of online news stories using social media reactions

Characterizing the life cycle of online news stories using social media reactions

A Review of Usage and Applications of Social Media Analytics

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

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