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  • 1.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis.

     

    Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis.

     

    With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.

  • 2.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    On social interaction metrics: social network crawling based on interestingness2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    With the high use of online social networks we are entering the era of big data. With limited resources it is important to evaluate and prioritize interesting data. This thesis addresses the following aspects of social network analysis: efficient data collection, social interaction evaluation and user privacy concerns. It is possible to collect data from online social networks via their open APIs. However, a systematic and efficient collection of online social networks data is still challenging. To improve the quality of the data collection process, prioritizing methods are statistically evaluated. Results suggest that the collection time can be reduced by up to 48% by prioritizing the collection of posts. Evaluation of social interactions also require data that covers all the interactions in a given domain. This has previously been hard to do, but the proposed crawler is capable of extracting all social interactions from a given page. With the extracted data it is for instance possible to illustrate indirect interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have been developed. These methods are evaluated with the too Linguistic Inquiry and Word Count. The privacy of the content produced; and the users’ private information provided on social networks is important to protect. Users must be aware of the consequence of posting in online social networks in terms of privacy. Methods to protect user privacy are presented. The proposed crawler in this thesis has, over the period of 20 months, collected over 38 million posts from public pages on Facebook covering: 4 billion likes and 340 million comments from over 280 million users. The performed data collection yielded one of the largest research dataset of social interactions on Facebook today, enabling qualitative research in form of social network analysis.

  • 3.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boldt, Martin
    Blekinge Institute of Technology, School of Computing.
    Johnson, Henric
    Blekinge Institute of Technology, School of Computing.
    Privacy threats related to user profiling in online social networks2012Conference paper (Refereed)
    Abstract [en]

    The popularity of Online Social Networks (OSNs) has increased the visibility of users profiles and interactions performed between users. In this paper we structure different privacy threats related to OSNs and describe six different types of privacy threats. One of these threats, named public information harvesting, is previously not documented so we therefore present it in further detail by also presenting the results from a proof-of-concept implementation of that threat. The basis of the attack is gathering of user interactions from various open groups on Facebook which then is transformed into a social interaction graph. Since the data gathered from the OSN originates from open groups it could be executed by any third-party connected to the Internet independently of the users' privacy settings. In addition to presenting the different privacy threats we also we propose a range of different protection techniques.

  • 4.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Borg, Anton
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Bródka, Piotr
    Wrocław University of Technolog, POL.
    Predicting User Participation in Social Media2016In: Lecture Notes in Computer Science / [ed] Wierzbicki A., Brandes U., Schweitzer F., Pedreschi D., Springer, 2016, Vol. 9564, p. 126-135Conference paper (Other academic)
    Abstract [en]

    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 ...

  • 5.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Bródka, Piotr
    Wrocław University of Science and Technology, POL.
    Boldt, Martin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Do We Really Need To Catch Them All?: A New User-Guided Social Media Crawling Method2017In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 19, no 12, article id 686Article in journal (Refereed)
    Abstract [en]

    With the growing use of popular social media services like Facebook and Twitter it is hard 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 Facebook public pages, containing 368 million users and 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%.What is more, the social network constructed from the 20% sample has more than 75% of the users and edges compared to the social network created from all posts, and has very similar degree distribution.

  • 6.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Bródka, Piotr
    Wrocƚaw University of Technology, POL.
    Borg, Anton
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Seed selection for information cascade in multilayer networks2018In: Studies in Computational IntelligenceVolume , 2018, Pages -436 / [ed] Cherifi H.,Cherifi C.,Musolesi M.,Karsai M., Springer-Verlag New York, 2018, Vol. 689, p. 426-436Conference paper (Refereed)
    Abstract [en]

    Information spreading is an interesting field in the domain of online social media. In this work, we are investigating how well different seed selection strategies affect the spreading processes simulated using independent cascade model on eighteen multilayer social networks. Fifteen networks are built based on the user interaction data extracted from Facebook public pages and tree of them are multilayer networks downloaded from public repository (two of them being Twitter networks). The results indicate that various state of the art seed selection strategies for single-layer networks like K-Shell or VoteRank do not perform so well on multilayer networks and are outperformed by Degree Centrality.

  • 7.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Bródka, Piotr
    Wrocƚaw University of Technology, POL.
    Borg, Anton
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Finding Influential Users in Social Media Using Association Rule Learning2016In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 18, no 5Article in journal (Refereed)
    Abstract [en]

    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.

  • 8.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, School of Engineering, Department of Interaction and System Design.
    Evertsson, Daniel
    Blekinge Institute of Technology, School of Engineering, Department of Interaction and System Design.
    S-UDDI: using Web services, the Secure and Trustworthy way2005Independent thesis Advanced level (degree of Master (One Year))Student thesis
    Abstract [en]

    SOA and especially Web services are a big evolving market these days. SOA typically uses Web services when interacting between different parts of applications. Methods to easily discover the Web services must exist. For this UDDI has been introduced. The current implementation of UDDI has a weak security model. We have developed an extension to this model, which interacts with the current solution to make it more secure. Our solution, S-UDDI, enable a way to find and publish Web services in a secure and trustworthy way.

  • 9.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Nia, Roozbeh
    Boldt, Martin
    Blekinge Institute of Technology, School of Computing.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wu, S. Felix
    Crawling Online Social Networks2015In: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015), IEEE Computer Society, 2015, p. 9-16Conference paper (Refereed)
    Abstract [en]

    Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.

  • 10.
    Erlandsson, Fredrik
    et al.
    Blekinge Institute of Technology, School of Computing.
    Nia, Roozbeh
    Johnson, Henric
    Blekinge Institute of Technology, School of Computing.
    Wu, Felix
    Making social interactions accessible in online social networks2013In: Information Services and Use, ISSN 0167-5265, E-ISSN 1875-8789, Vol. 33, no 2, p. 113-117Article in journal (Refereed)
    Abstract [en]

    Online Social Networks (OSNs) have changed the way people use the internet. Over the past few years these platforms have helped societies to organize riots and revolutions such as the Arab Spring or the Occupying Movements. One key fact in particular is how such events and organizations spread through out the world with social interactions, though, not much research has been focused on how to efficiently access such data and furthermore, make it available to researchers. While everyone in the field of OSN research are using tools to crawl this type of networks our approach differs significantly from the other tools out there since we are getting all interactions related to every single post. In this paper we show means of developing an efficient crawler that is able to capture all social interactions on public communities on OSNs such as Facebook.

  • 11. Nia, Roozbeh
    et al.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Bhattacharyya, Prantik
    Rahman, Mohammad Rezaur
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wu, S. Felix
    SIN: A Platform to Make Interactions in Social Networks Accessible2012In: Proceedings of the 2012 ASE International Conference on Social Information, IEEE conference proceedings, 2012, p. 205-214Conference paper (Refereed)
    Abstract [en]

    Online Social Networks (OSNs) are popular platforms for interaction, communication and collaboration between friends. In this paper we develop and present a new platform to make interactions in OSNs accessible. Most of today's social networks, including Facebook, Twitter, and Google+ provide support for third party applications to use their social network graph and content. Such applications are strongly dependent on the set of software tools and libraries provided by the OSNs for their own development and growth. For example, third party companies like CNN provide recommendation materials based on user interactions and user's relationship graph. One of the limitations with this graph (or APIs) is the segregation from the shared content. We believe, and present in this paper, that the content shared and the actions taken on the content, creates a Social Interaction Network (SIN). As such, we extend Facebook's current API in order to allow applications to retrieve a weighted graph instead of Facebooks unweighted graph. Finally, we evaluate the proposed platform based on completeness and speed of the crawled results from selected community pages. We also give a few example uses of our API on how it can be used by third party applications.

  • 12. Nia, Roozbeh
    et al.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wu, S. Felix
    Leveraging Social Interactions to Suggest Friends2013In: IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW), IEEE Computer Society, 2013, p. 386-391Conference paper (Refereed)
    Abstract [en]

    Over the past decade Online Social Networks (OSNs) have made it possible for people to stay in touch with people they already know in real life; although, they have not been able to allow users to grow their personal social network. Existence of many successful dating and friend finder applications online today show the need and importance of such applications. In this paper, we describe an application that leverages social interactions in order to suggest people to users that they may find interesting. We allow users to expand their personal social network using their own interactions with other users on public pages and groups in OSNs. We finally evaluate our application by selecting a random set of users and asking them for their honest opinion.

  • 13.
    Pham, Phuong
    et al.
    UC Davis, USA.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wu, Felix
    UC Davis, USA.
    Social Coordinates: A Scalable Embedding Framework for Online Social Networks2017In: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing, ACM Digital Library, 2017, p. 191-196Conference paper (Refereed)
    Abstract [en]

    We present a scalable framework to embed nodes of a large social network into an Euclidean space such that the proximity between embedded points reflects the similarity between the corresponding graph nodes. Axes of the embedded space are chosen to maximize data variance so that the dimension of the embedded space is a parameter to regulate noise in data. Using recommender system as a benchmark, empirical results show that similarity derived from the embedded coordinates outperforms similarity obtained from the original graph-based measures.

  • 14.
    Wang, Teng
    et al.
    University of California - Davis, USA.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wu, Shyhtsun Felix
    University of California - Davis, USA.
    Mining User Deliberation and Bias in Online Newsgroups: A Dynamic View2015In: Proceedings of the 2015 ACM on Conference on Online Social Networks, ACM , 2015, p. 209-219Conference paper (Refereed)
    Abstract [en]

    Social media is changing many different aspects of our lives. By participating in online discussions, people exchange opinions on various topics, shape their stances, and gradually form their own characteristics. In this paper, we propose a framework for identifying online user characteristics and understanding the formation of user deliberation and bias in online newsgroups. In the first section of the paper, we propose a dynamic user-like graph model for recognizing user deliberation and bias automatically in online newsgroups. In addition, we evaluate our identification results with linguistic features and implement this model in our SINCERE system as a real-time service. In the second section, after applying this model to two large online newsgroups, we analyze the influence of early discussion context on the formation of user characteristics. Our conclusion is that user deliberation and bias are a product of situations, not simply dispositions: confronting disagreement in unfamiliar circumstances promotes more consideration of different opinions, while recurring conflict in familiar circumstances evokes close-minded behavior and bias. Based on this observation, we also build a supervised learning model to predict user deliberation and bias at an early online life-stage. Our results show that having only the first three months of users' interaction data generates an F1 accuracy level of around 70% in predicting user deliberation and bias in online newsgroups. This work has practical significance for people who design and maintain online newsgroups. It yields new insights into opinion diffusion and has wide potential applications in politics, education, and online social media.

  • 15. Wang, Teng
    et al.
    Wang, K. C.
    Erlandsson, Fredrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Wu, S. Felix
    Faris, Robert W.
    The influence of feedback with different opinions on continued user participation in online newsgroups2013In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, IEEE Computer Society, 2013, p. 388-395Conference paper (Refereed)
    Abstract [en]

    With the popularity of social media in recent years, it has been a critical topic for social network designer to understand the factors that influence continued user participation in online newsgroups. Our study examines how feedback with different opinions is associated with participants' lifetime in online newsgroups. Firstly, we propose a new method of classifying different opinions among user interaction contents. Generally, we leverage user behavior information in online newsgroups to estimate their opinions and evaluate our classification results based on linguistic features. In addition, we also implement this opinion classification method into our SINCERE system as a real-time service. Based on this opinion classification tool, we use survival analysis to examine how others' feedback with different opinions influence continued participation. In our experiment, we analyze more than 88,770 interactions on the official Occupy LA Facebook page. Our final result shows that not only the feedback with the same opinions as the user, but also the feedback with different opinions can motivate continued user participation in online newsgroup. Furthermore, an interaction of feedback with both the same and different opinions can boost user continued participation to the greatest extent. This finding forms the basis of understanding how to improve online service in social media. Copyright 2013 ACM.

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