Predicting User Participation in Social Media

Erlandsson, Fredrik; Borg, Anton; Johnson, Henric; Bródka, Piotr

doi:10.1007/978-3-319-28361-6_10

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…However, future users need to be aware of the limitation and potential bias enforced with the USMC method, i.e., that the resulting data exclude low interaction volumes. Examples of analyses made possible using data crawled by the USMC method include community detection [9] and identification of influential users [10,11].…”

Section: Motivationmentioning

confidence: 99%

Do We Really Need to Catch Them All? A New User-Guided Social Media Crawling Method

Erlandsson

Bródka

Boldt

et al. 2017

Entropy

Self Cite

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Abstract:With the growing use of popular social media services like Facebook and Twitter it is challenging to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance. In this work we present a novel User-guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected to cover as many user interactions as possible. USMC is validated by crawling 160 public Facebook pages, containing content from 368 million users including 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%. In addition, the social network constructed from the 20% sample contains more than 75% of the users and edges compared to the social network created from all posts, and it has similar degree distribution.

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Section: Motivationmentioning

confidence: 99%

Do We Really Need to Catch Them All? A New User-Guided Social Media Crawling Method

Erlandsson

Bródka

Boldt

et al. 2017

Entropy

Self Cite

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“…Initial research used association rule learning to identify influential users and predict user participation in online social networks [5]. Association rule learning has been previously used in social network and social media analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies have tried to identify user influence; however, most have used Page Rank Centrality [2,3] or Degree Centrality [3,4] based approaches to identify influential users. This paper builds on the initial discoveries on association rule learning in social networking sites: [5].…”

Section: Introductionmentioning

confidence: 99%

Finding Influential Users in Social Media Using Association Rule Learning

Erlandsson

Bródka

Borg

et al. 2016

Entropy

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Abstract:Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

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“…Comments are composed of positive opinions that create a contagion effect, while criticism or negative comments also influence users' decisions [9], by which, it is important to classify and analyze users' sentiments to know their reactions to a certain topic or tweet. Supported by association rule learning and machine learning algorithms, it is possible to discover the relationships between words that associate sentiments [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Sierra-Linan³

et al. 2022

IJACSA

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Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions. One of the most popular social media for expressing their feelings towards events that occur is Twitter. The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter. This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis. To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets. Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud. The study uses tweets published in the month of May 2022. Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively. Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method. Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out.

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Predicting User Participation in Social Media

Cited by 8 publications

References 24 publications

Do We Really Need to Catch Them All? A New User-Guided Social Media Crawling Method

Do We Really Need to Catch Them All? A New User-Guided Social Media Crawling Method

Finding Influential Users in Social Media Using Association Rule Learning

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

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