Behavioural role analysis for multi-faceted communication campaigns in Twitter

Lazaridou, Paraskevi; Ntalla, Athanasia C.; Novak, Jasminko

doi:10.1145/2908131.2908202

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…al [6] and Lazaridou et. al [13] limit themselves to identify a small number (often 3-5) of major, coarse-grained groups, roughly corresponding the upper levels of our detection hierarchy. Du et.…”

Section: Related Workmentioning

confidence: 99%

“…al [5] provide a somewhat higher number of rules (still lower than ours), but only give generic descriptions. All of these previously mentioned methods are constrained on just detecting the structure by unsupervised learning: clustering via K-Means [13], EM [6] or via topic models [5], leaving the analysis entirely to human experts. In terms of classification, Varol et al [18] fully rely on such human expertise, using similarity matrices and handcrafted rules.…”

Section: Related Workmentioning

confidence: 99%

“…In this work we aim to use features that cover significant and complementary aspects of users and are well established in the literature [6,13,1]. In addition, it should be feasible to compute in large scale so that data is commonly available and incur moderate cost to compute.…”

Section: Feature Engineeringmentioning

confidence: 99%

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Detecting and analyzing fine-grained user roles in social media?

Kästner

Fischer

2023

COMSIS J

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While identifying specific user roles in social media -in particular bots or spammers- has seen significant progress, generic and all-encompassing user role classification remains elusive on the large data sets of today?s social media. Yet, such broad classifications enable a deeper understanding of user interactions and pave the way for longitudinal studies, capturing the evolution of users such as the rise of influencers. Studies of generic roles have been performed predominantly in a small scale, establishing fundamental role definitions, but relying mostly on ad-hoc, data set-dependent rules that need to be carefully hand-tuned. We build on those studies and provide a largely automated, scalable detection of a wide range of roles. Our approach clusters users hierarchically on salient, complementary features such as their actions, their ability to trigger reactions and their network positions. To associate these clusters with roles, we use supervised classifiers: trained on human experts on completely new media, but transferable on related data sets. Furthermore, we employ the combination of samples in order to improve scalability and allow probabilistic assignments of user roles. Our evaluation on Twitter indicates that a) stable and reliable detection of a wide range of roles is possible b) the labeling transfers well as long as the fundamental properties don?t strongly change between data sets and c) the approaches scale well with little need for human intervention.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Detecting and analyzing fine-grained user roles in social media?

Kästner

Fischer

2023

COMSIS J

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“…Dan thinks this case is only to study the data in China, but there are similar cases all over the world. They think that in this way, they can do the cross-cultural and cross-platform comparison to study the effect of users' data sharing behavior, to break the bubbles, and to understand and compare how different groups of users from various future and cultural bubbles consume and interact with similar topics (Lazaridou, Ntalla, & Novak, 2016). In this study, she proposed that e-health and personal trackers sharing personal health data on social networks is just one case.…”

Section: Dan Wu: "Research On Cross-cultural and Cross-language Users In Academic Social Network And Ehealth Social Media Context"mentioning

confidence: 99%

Breaking Social Media Bubbles for Information Globalization: A Cross-Cultural and Cross-Language User-Centered Sense-Making Approach

Liu

2020

Data and Information Management

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With the globalization of data, online social media plays an active role in spreading information and classifying people, and thinking about how to break the solidification of algorithms becomes critical. Current algorithmic research in the social media space often focuses on a single service or language, mainly due to the lack of a way to connect the different bubbles. The panel speakers described their various research activities in which they presented different perspectives on how to break the bubble. This article provides a summary of this interactive panel.

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Scalable and Explainable User Role Detection in Social Media

Kästner

Fischer

2021

New Trends in Database and Information Systems

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Behavioural role analysis for multi-faceted communication campaigns in Twitter

Cited by 5 publications

References 4 publications

Detecting and analyzing fine-grained user roles in social media?

Detecting and analyzing fine-grained user roles in social media?

Breaking Social Media Bubbles for Information Globalization: A Cross-Cultural and Cross-Language User-Centered Sense-Making Approach

Scalable and Explainable User Role Detection in Social Media

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