Social Media Visual Analytics

Chen, Siming; Lin, Lijing; Yuan, Xiaoru

doi:10.1111/cgf.13211

Cited by 92 publications

(58 citation statements)

References 88 publications

(176 reference statements)

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“…As we collect research works from a diverse set of domains such as social media, finance, and cybersecurity, the scope of anomaly detection in our survey is broader than the scope identified in specific domains. For example, e.g., Chen et al [5] identify data outside normal ranges of attributes as anomalies in social media while in the field of cybersecurity, anomalies refer to malware, insider threats, and targeted attacks [13], [14]. In our work, anomalies refer to frauds, spam, intrusion, sudden increases in the volume of data, and periodic patterns of users, etc.…”

Section: A Terminologymentioning

confidence: 93%

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Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behaviorrelated data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss findings and potential research directions.• We categorize four user behaviors, including social interaction, travel, network communication, and transaction based on the data collected from specific data sources. We extract four common data types from these four behaviors, including text, network, spatiotemporal information, and multidimensional data.• We review how research works use visualization techniques combined with interaction methods to analyze anomalous user behaviors. We extract six

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Section: A Terminologymentioning

confidence: 93%

“…A variety of data can be extracted from user behaviors across different domains. By analyzing multiple attributes of these data, we summarize four common data types including text, network, spatiotemporal information, and multidimensional data [3], [5]. A brief explanation for each data types is described as follows.…”

Section: Taxonomymentioning

confidence: 99%

Visual Analytics of Anomalous User Behaviors: A Survey

Shi

Liu

Tong

et al. 2020

IEEE Trans. Big Data

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“…Social media sites are rich sources of information that enable researchers to perform different kinds of analysis, including event analysis and cross-platform information linking [2,5,8,23], visual sentiment analysis [20], opinion diffusion analysis [6,43,46], and text analysis and topic modeling [15,25]. A thorough review of visual analytics research using social media data is presented in [7,42].…”

Section: Visual Social Media Analyticsmentioning

confidence: 99%

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

Khayat

Karimzadeh

Zhao

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.

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“…Since the term Visual Analytics (VA) was coined in 2004 [WT04, TC05], there have been hundreds of software systems reported in the literature for supporting various VA workflows (e.g., [ERT∗17, LGH∗17,CLY17]). As articulated by Thomas and Kielman [TK09] and Robertson et.…”

Section: Related Workmentioning

confidence: 99%

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Chen

Ebert

2019

Computer Graphics Forum

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Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the cost‐benefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect “symptoms”, “causes”, “remedies”, and “side‐effects”, and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time‐efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial‐and‐error in the lifecycle of VA system development.

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Social Media Visual Analytics

Cited by 92 publications

References 88 publications

Visual Analytics of Anomalous User Behaviors: A Survey

Visual Analytics of Anomalous User Behaviors: A Survey

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

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