| 1. |
- Hanteer, Obaida, et al.
(author)
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From Interaction to Participation : The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter
- 2018
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In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). - : IEEE Computer Society. - 9781538660515 ; , s. 531-534
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Conference paper (peer-reviewed)abstract
- In the context of community detection in online social media, a lot of effort has been put into the definition of sophisticated network clustering algorithms and much less on the equally crucial process of obtaining high-quality input data. User-interaction data explicitly provided by social media platforms has largely been used as the main source of data because of its easy accessibility. However, this data does not capture a fundamental and much more frequent type of participatory behavior where users do not explicitly mention others but direct their messages to an invisible audience following a common hashtag. In the context of multiplex community detection, we show how to construct an additional data layer about user participation not relying on explicit interactions between users, and how this layer can be used to find different types of communities in the context of Twitter political communication.
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| 2. |
- Krause, Robert W., et al.
(author)
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Missing Network Data A Comparison of Different Imputation Methods
- 2018
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In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538660515 - 9781538660522 ; , s. 159-163
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Conference paper (peer-reviewed)abstract
- This paper compares several imputation methods for missing data in network analysis on a diverse set of simulated networks under several missing data mechanisms. Previous work has highlighted the biases in descriptive statistics of networks introduced by missing data. The results of the current study indicate that the default methods (analysis of available cases and null-tie imputation) do not perform well with moderate or large amounts of missing data. The results further indicate that multiple imputation using sophisticated imputation models based on exponential random graph models (ERGMs) lead to acceptable biases even under large amounts of missing data.
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| 3. |
- Sainudiin, Raazesh, et al.
(author)
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Rejecting the Null Hypothesis of Apathetic Retweeting of US Politicians and SPLC-defined Hate Groups in the 2016 US Presidential Election
- 2018
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In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). - : IEEE. - 9781538660515 ; , s. 250-253
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Conference paper (peer-reviewed)abstract
- We characterize the Twitter networks of both major presidential candidates, Donald Trump and Hillary Clinton, with various American hate groups defined by the US Southern Poverty Law Center (SPLC). We further examined the Twitter networks for Bernie Sanders, Ted Cruz, and Paul Ryan, for 9 weeks around the 2016 election (4 weeks prior to the election and 4 weeks post-election). By carefully accounting for the observed heterogeneity in the Twitter activity levels across individuals under the null hypothesis of apathetic retweeting that is formalized as a random network model based on the directed, multi-edged, self-looped, configuration model, our data revealed via a generalized Fisher's exact test that there were significantly many Twitter accounts linked to SPLC-defined hate groups belonging to seven ideologies (Anti-Government, Anti-Immigrant, Anti-LGBT, Anti-Muslim, Alt-Right, Neo-Nazi, and White-Nationalist) and also to @realDonaldTrump relative to the accounts of the other four politicians. The exact hypothesis test uses Apache Spark's distributed sort and join algorithms to produce independent samples in a fully scalable way from the null model.
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| 4. |
- Varga, Stefan, et al.
(author)
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Information requirements for national level cyber situational awareness
- 2018
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In: Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538660515 ; , s. 774-781
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Conference paper (peer-reviewed)abstract
- As modern societies become more dependent on IT services, the potential impact both of adversarial cyberattacks and non-adversarial service management mistakes grows. This calls for better cyber situational awareness-decision-makers need to know what is going on. The main focus of this paper is to examine the information elements that need to be collected and included in a common operational picture in order for stakeholders to acquire cyber situational awareness. This problem is addressed through a survey conducted among the participants of a national information assurance exercise conducted in Sweden. Most participants were government officials and employees of commercial companies that operate critical infrastructure. The results give insight into information elements that are perceived as useful, that can be contributed to and required from other organizations, which roles and stakeholders would benefit from certain information, and how the organizations work with creating cyber common operational pictures today. Among findings, it is noteworthy that adversarial behavior is not perceived as interesting, and that the respondents in general focus solely on their own organization.
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