5 research outputs found
Finding Influential Users in Social Media Using Association Rule Learning
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
Do we really need to catch them all? A new User-guided Social Media Crawling method
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
Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm
Abstract: 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
Disentangling Technostress in Social Streaming Services: The Impact of Perceived Eustress and Distress on User Participation and Engagement
Technostress resulting from the use of social media and social streaming services is usually associated with negative consequences, such as lower user participation and engagement. This paper, however, finds evidence that the perception of stress in association with the use of social streaming services also positively impacts user participation and engagement by disentangling the concept of stress into eustress and distress. Data from 147 social streaming services users were collected and analyzed with structural equation modeling. The results confirm that perceived eustress positively affects user participation and engagement, while perceived distress decreases user participation and engagement. Separating the concept of user participation and engagement into benign and malicious user participation and engagement reveals that eustress is positively related to benign user participation and engagement, whereas perceived distress is positively associated with malicious user participation and engagement
Predicting User Participation in Social Media
Abstract Online social networking services like Facebook provides a popular way for users to participate in different communication groups and discuss relevant topics with each other. While users tend to have an impact on each other, it is important to better understand and ..