Astroturfing detection in social media: a binary n‐gram–based approach

Peng, Jian; Detchon, Sam; Choo, Kim‐Kwang Raymond; Ashman, Helen

doi:10.1002/cpe.4013

Cited by 36 publications

(16 citation statements)

References 37 publications

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“…The approach consisted of extracting the user's writing style, uses the k-Nearest Neighbors algorithm (k-NN) to evaluate the post content and identify the user, and uses a continuous updating of the user baseline to support existing trends and seasonality issues of the user's posts. The findings reported by the authors in this special issue, as well as those of Peng et al [14]) and Peng et al [15]) demonstrated the potential to identify users based on the textual contents of their postings on social media.…”

Section: Social Networksupporting

confidence: 59%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

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In the increasingly inter-connected society, multimedia social networks (MSN) have become a 'mainstream' tool used by online users to connect and share contents with other users 24/7 in real-time. It is, therefore, unsurprising that MSNs have become a salient area of inquiry by computer scientists and computer security researchers. For example, researchers need to design intelligent computing and soft computing technologies to improve multimedia system functions, efficiency and performance, as well as improving user's sharing experiences (e.g. using recommendation systems and more effective algorithms). Ensuring the security of users and data are also an ongoing topic of interest and importance due to the ease in producing and sharing user and multimedia content using MSNs. In recent years, we have seen advances in multimedia system security and soft computing for MSN applications, such as Bhard^security mechanisms (e.g. protocols and methodologies) and Bsoft^computing methods (e.g. machine learning and rough set), as well as research efforts in trust assessment, risk management and social factors to understand the trade-off between the effectiveness and security in MSNs.This special issue is dedicated to the reporting of state-of-the-art and recent advancements in this emerging area of enquiry. We received 26 submissions for this special issue, of which 12 were accepted for publication (i.e. 46 % acceptance rate). Each paper went through a rigorous peer review process, in addition to multiple follow-up rounds with the authors. A summary of the papers is categorized and presented below.

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Section: Social Networksupporting

confidence: 59%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

Self Cite

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“…Similarly, classification methods such as K-Nearest Neighbors [21], Gradient Boosted Decision Trees [23] and Support Vector Machines [17] have been used for identifying user based on their profiles. In addition, some works follow an information retrieval approach and estimate distance metrics for ranking possible users for a given content [22,33]. In this work, we employ a single-label multi-class model approach, where each class correspond to a specific user, and the content could be assigned exclusively to one user.…”

Section: Related Workmentioning

confidence: 99%

User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images

Gomez¹,

Ibarra-Manzano²,

Almanza-Ojeda³

2017

RCS

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User identification in social media is of crucial interest for companies and organizations for purposes of marketing, e-commerce, security and demographics. In this paper, we aim to identify users from Pinterest, a platform where users post pins, a combination of an image and a short text. This type of multi-modal content is very common nowadays, since it is a natural way in which users express their interests, emotions and opinions. Thus, the goal is to identify the user that would post a particular pin. For solving the problem, we propose a two-phase classification model. In a first phase, we train independent classifiers from image data, using a deep learning representation, and from text data, using a bag-of-words representation. During testing we apply a cascade fusion of the classifiers. In a second phase, we refine the output of the cascade for each test pin by selecting the top most likely users for the test pin and re-weighting their corresponding output in the cascade by their similarity with the test pin. Our experiments show that the problem is very hard because several reasons with the data distribution, but they also show promising results.

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“…The IRA's accounts have been created in such a way that they are portrayed as real American accounts. Masking the sponsor of a message such that it appears to originate, and be supported by, grassroots participants is also known as astroturfing (Peng et al, 2017). Based on a 2018 Pew Report, 53% of the Americans participate in some form of civic or political activities on social media during the year (Anderson et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm

Nasrin¹,

Choo²,

Ko³

et al. 2019

Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaga

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Social media has reportedly been (ab)used by Russian troll farms to promote political agendas. Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives. Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord. While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion. In this paper, we study the impact the number of labeled troll accounts has on detection performance. We analyze the use of self-supervision with less than 100 troll accounts as training data. We improve classification performance by nearly 4% F1. Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry. Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts. Overall, we improve on models based on words features by ∼9% F1.

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Astroturfing detection in social media: a binary n‐gram–based approach

Cited by 36 publications

References 37 publications

Guest Editorial: Multimedia Social Network Security and Applications

Guest Editorial: Multimedia Social Network Security and Applications

User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images

How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm

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