Crawling Online Social Networks

Erlandsson, Fredrik; Nia, Roozbeh; Boldt, Martin; Johnson, Henric; Wu, Shanchan

doi:10.1109/enic.2015.10

Cited by 29 publications

(16 citation statements)

References 19 publications

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“…We used our Social Interactive Networking and Conversation Entropy Ranking Engine (SINCERE) data (Erlandsson et al, 2015). It was crawled via Facebook API from public interaction data on almost all Facebook fan pages and public communities from 2008 to early 2018.…”

Section: Datamentioning

confidence: 99%

How Do Fake News Propagators Exploit Social Algorithms to Promote Their Contents?

2020

Proceedings of the International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) An

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Social media has currently become a primary platform to consume and exchange information. Instead of browsing through media source websites we are interested, we have enabled social media to deliver related contents to us. As a black box, social algorithms were designed and trained to pick up, filter, and rank the most relevant and desired contents to be delivered to each individual one of us. In addition, social media allows us to add more contents as a comment on an original post, which we call user-created content. Unfortunately, a lot of fake news contents have been created and disseminated in Facebook fan pages. Many of them could draw many attentions from users. Our research question is how do fake news propagators successfully make their contents be more visible and disseminate it to many users through social algorithms? In this work, we explored behavior and activities of fake news propagators and their trapped recipient to understand how social algorithms widely delivered those contents to many users on Facebook fan pages. From our SINCERE data, we extracted top 300 fake news comments that got the highest number of participants as our data set. We statistically analyzed the data set based on our hypotheses, which were mainly from our previous work of reverse-engineering for content delivery algorithms in social media and from what we observed from the SINCERE data. Our main contribution for this work is to understand behaviors, activities, and strategies of fake news propagators who successfully disseminated their contents to many users through social algorithms on Facebook.

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Section: Datamentioning

confidence: 99%

How Do Fake News Propagators Exploit Social Algorithms to Promote Their Contents?

2020

Proceedings of the International Conferences on ICT, Society and Human Beings (ICT 2020), Connected Smart Cities (CSC 2020) An

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“…Our Facebook dataset [34] is crawled from public pages by using Facebook Graph API. In this paper, we only consider the public pages like CBS News, Fox News and New York Times, etc.…”

Section: Data Collectionmentioning

confidence: 99%

Multi-view Community Detection in Facebook Public Pages

Xin

Lai

Chapman

et al. 2019

Communications in Computer and Information Science

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Community detection in social networks is widely studied because of its importance in uncovering how people connect and interact. However, little attention has been given to community structure in Facebook public pages. In this study, we investigate the community detection problem in Facebook newsgroup pages. In particular, to deal with the diversity of user activities, we apply multi-view clustering to integrate different views, for example, likes on posts and likes on comments. In this study, we explore the community structure in not only a given single page but across multiple pages. The results show that our method can effectively reduce isolates and improve the quality of community structure.

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“…The data used in this study have been obtained from the crawler described by Erlandsson et al [37]. This crawler gathers complete posts from Facebook.…”

Section: Data Modelmentioning

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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Crawling Online Social Networks

Cited by 29 publications

References 19 publications

How Do Fake News Propagators Exploit Social Algorithms to Promote Their Contents?

How Do Fake News Propagators Exploit Social Algorithms to Promote Their Contents?

Multi-view Community Detection in Facebook Public Pages

Finding Influential Users in Social Media Using Association Rule Learning

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