Publication News

Yeeeha, our paper “Sorry, I was hacked”—A Classification of Compromised Twitter Accounts has been accepted at the ACM Symposium on Applied Computing 🙂 In particular, it has been accepted for the Social Network and Media Analysis (SONAMA) track. Our work features an analysis of Twitter whose Twitter accounts have been compromised and it aims at analysing how users deal with this account comprimising. I’m really looking forward to presenting our work at Dongguk University, Gyeongju, Korea. If you are interested in this work, I just uploaded the pdf to the publications section or contact me 🙂

Our two journal articles (finally) have been published too. The first article elaborates on how to support and guide users during collaborative content creation at the Journal on Future Generation Computer Systems (impact factor 1.978). The second article is about how text similarity measures influence the quality of hashtag recommendations for tweets and is published in Springer’s Social Network Analysis and Mining Journal.

Furthermore, my dissertation has been featured in Datenbank-Spektrum, the Journal of  the German Computer Society.

  • E. Zangerle, “Dissertationen: leveraging recommender systems for the creation and maintenance of structure within collaborative social media platforms,” Datenbank-spektrum, vol. 13, iss. 3, p. 239, 2013. doi:10.1007/s13222-013-0138-6
    Bibtex BibTeX Abstract Abstract

    During the last decade, the web transformed from a web of information consumers to a web of information producers. In particular, the advent of online social media platforms is hugely responsible for this shift as people now actively post information in knowledge bases, engage in online communities and contribute to social media platforms. Hence, a vast amount of new information is produced each day. This publicly available data is an invaluable source of information which still is to be fully exploited. Due to the broad span of users of such systems (originating from different cultures and backgrounds, speaking different languages, etc.), the information provided features a limited amount of common structure, as e.g., objects are named differently and information is structured differently. This is a severe constraint in regards to the performance of search facilities. This thesis proposes to facilitate recommender systems to create and maintain a common structure within collaborative social media platforms aiming at improving search performance. For this purpose, two different recommender systems for two showcase platforms are presented. The first recommender system provides recommendations for structuring information within a semistructured information system whereas the second recommender systems is a hashtag recommender system for microblogging services.

    @article{dbspektrum,
    title = {Dissertationen: Leveraging Recommender Systems for the Creation and Maintenance of Structure within Collaborative Social Media Platforms},
    author = {Eva Zangerle},
    doi = {10.1007/s13222-013-0138-6},
    year = {2013},
    date = {2013-01-01},
    journal = {Datenbank-Spektrum},
    volume = {13},
    number = {3},
    pages = {239},
    abstract = {During the last decade, the web transformed from a web of information consumers to a web of information producers. In particular, the advent of online social media platforms is hugely responsible for this shift as people now actively post information in knowledge bases, engage in online communities and contribute to social media platforms. Hence, a vast amount of new information is produced each day. This publicly available data is an invaluable source of information which still is to be fully exploited. Due to the broad span of users of such systems (originating from different cultures and backgrounds, speaking different languages, etc.), the information provided features a limited amount of common structure, as e.g., objects are named differently and information is structured differently. This is a severe constraint in regards to the performance of search facilities. This thesis proposes to facilitate recommender systems to create and maintain a common structure within collaborative social media platforms aiming at improving search performance. For this purpose, two different recommender systems for two showcase platforms are presented. The first recommender system provides recommendations for structuring information within a semistructured information system whereas the second recommender systems is a hashtag recommender system for microblogging services.},
    keywords = {},
    pubstate = {published},
    tppubtype = {article}
    }

  • E. Zangerle, W. Gassler, and G. Specht, “On the impact of text similarity functions on hashtag recommendations in microblogging environments,” Social network analysis and mining, vol. 3, iss. 4, pp. 889-898, 2013. doi:10.1007/s13278-013-0108-x
    Bibtex BibTeX Abstract Abstract PDF Download PDF

    Microblogging applications such as Twitter are experiencing tremendous success. Microblog users utilize hashtags to categorize posted messages which aim at bringing order to the myriads of microblog messages. However, the percentage of messages incorporating hashtags is small and the used hashtags are very heterogeneous as hashtags may be chosen freely and may consist of any arbitrary combination of characters. This heterogeneity and the lack of use of hashtags lead to significant drawbacks in regards to the search functionality as messages are not categorized in a homogeneous way. In this paper, we present an approach for the recommendation of hashtags suitable for the message the user currently enters which aims at creating a more homogeneous set of hashtags. Furthermore, we present a detailed study on how the similarity measures used for the computation of recommendations influence the final set of recommended hashtags.

    @article{snam,
    title = {On the impact of text similarity functions on hashtag recommendations in microblogging environments},
    author = {Eva Zangerle and Wolfgang Gassler and G\"{u}nther Specht},
    url = {https://www.evazangerle.at/wp-content/uploads/2017/06/snam.pdf},
    doi = {10.1007/s13278-013-0108-x},
    issn = {1869-5450},
    year = {2013},
    date = {2013-01-01},
    journal = {Social Network Analysis and Mining},
    volume = {3},
    number = {4},
    pages = {889-898},
    publisher = {Springer Vienna},
    abstract = {Microblogging applications such as Twitter are experiencing tremendous success. Microblog users utilize hashtags to categorize posted messages which aim at bringing order to the myriads of microblog messages. However, the percentage of messages incorporating hashtags is small and the used hashtags are very heterogeneous as hashtags may be chosen freely and may consist of any arbitrary combination of characters. This heterogeneity and the lack of use of hashtags lead to significant drawbacks in regards to the search functionality as messages are not categorized in a homogeneous way. In this paper, we present an approach for the recommendation of hashtags suitable for the message the user currently enters which aims at creating a more homogeneous set of hashtags. Furthermore, we present a detailed study on how the similarity measures used for the computation of recommendations influence the final set of recommended hashtags.},
    note = {(The final publication is available at link.springer.com.)},
    keywords = {},
    pubstate = {published},
    tppubtype = {article}
    }

  • E. Zangerle and G. Specht, ““sorry, i was hacked”: a classification of compromised twitter accounts,” in Proceedings of the 29th acm symposium on applied computing, Gyeongju, Korea, 2014-01-01 2014, p. 587–593. doi:10.1145/2554850.2554894
    Bibtex BibTeX Abstract Abstract PDF Download PDF

    Online social networks like Facebook or Twitter have become powerful information diffusion platforms as they have attracted hundreds of millions of users. The possibility of reaching millions of users within these networks not only attracted standard users, but also cyber-criminals who abuse the networks by spreading spam. This is accomplished by either creating fake accounts, bots, cyborgs or by hacking and compromising accounts. Compromised accounts are subsequently used to spread spam in the name of their legitimate owner. This work sets out to investigate how Twitter users react to having their account hacked and how they deal with compromised accounts. We crawled a data set of tweets in which users state that their account was hacked and subsequently performed a supervised classification of these tweets based on the reaction and behavior of the respective user. We find that 27.30% of the analyzed Twitter users change to a new account once their account was hacked. 50.91% of all users either state that they were hacked or apologize for any unsolicited tweets or direct messages.

    @inproceedings{sac14,
    title = {“Sorry, I was hacked": A Classification of Compromised Twitter Accounts},
    author = {Eva Zangerle and G\"{u}nther Specht},
    url = {https://www.evazangerle.at/wp-content/uploads/2017/06/sac14.pdf},
    doi = {10.1145/2554850.2554894},
    year = {2014},
    date = {2014-01-01},
    booktitle = {Proceedings of the 29th ACM Symposium on Applied Computing},
    pages = {587--593},
    publisher = {ACM},
    address = {Gyeongju, Korea},
    abstract = {Online social networks like Facebook or Twitter have become powerful information diffusion platforms as they have attracted hundreds of millions of users. The possibility of reaching millions of users within these networks not only attracted standard users, but also cyber-criminals who abuse the networks by spreading spam. This is accomplished by either creating fake accounts, bots, cyborgs or by hacking and compromising accounts. Compromised accounts are subsequently used to spread spam in the name of their legitimate owner. This work sets out to investigate how Twitter users react to having their account hacked and how they deal with compromised accounts.
    We crawled a data set of tweets in which users state that their account was hacked and subsequently performed a supervised classification of these tweets based on the reaction and behavior of the respective user. We find that 27.30% of the analyzed Twitter users change to a new account once their account was hacked. 50.91% of all users either state that they were hacked or apologize for any unsolicited tweets or direct messages.},
    keywords = {},
    pubstate = {published},
    tppubtype = {inproceedings}
    }

  • W. Gassler, E. Zangerle, and G. Specht, “Guided curation of semistructured data in collaboratively-built knowledge bases,” Journal on future generation computer systems, vol. 31, pp. 111-119, 2014. doi:10.1016/j.future.2013.05.008
    Bibtex BibTeX Abstract Abstract

    The collaborative curation of semistructured knowledge has become a popular paradigm on the web and also within enterprises. In such knowledge bases a common structure of the stored information is crucial for providing efficient and precise search facilities. However, the task of refining, extending and homogenizing knowledge and its structure is very complex. In this article we present two paradigms for the simplification of this task by providing guidance mechanisms to the user. Both paradigms aim at combining the power of automated extraction algorithms with the semantic awareness of human users to accomplish this refinement task.

    @article{fgcs,
    title = {Guided Curation of Semistructured Data in Collaboratively-built Knowledge Bases},
    author = {Wolfgang Gassler and Eva Zangerle and G\"{u}nther Specht},
    doi = {10.1016/j.future.2013.05.008},
    year = {2014},
    date = {2014-01-01},
    journal = {Journal on Future Generation Computer Systems},
    volume = {31},
    pages = {111-119},
    publisher = {Elsevier Science Publishers},
    abstract = {The collaborative curation of semistructured knowledge has become a popular paradigm on the web and also within enterprises. In such knowledge bases a common structure of the stored information is crucial for providing efficient and precise search facilities. However, the task of refining, extending and homogenizing knowledge and its structure is very complex. In this article we present two paradigms for the simplification of this task by providing guidance mechanisms to the user. Both paradigms aim at combining the power of automated extraction algorithms with the semantic awareness of human users to accomplish this refinement task.},
    note = {impact factor 1.978.},
    keywords = {},
    pubstate = {published},
    tppubtype = {article}
    }