Survey of Generative Methods for Social Media Analysis

Matwin, Stan; Milios, Aristides; Prałat, Paweł; Soares, Amílcar; Théberge, François

doi:10.48550/arxiv.2112.07041

Cited by 1 publication

(2 citation statements)

References 194 publications

(271 reference statements)

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“…It generates texts that are nearly indistinguishable from human-written texts: Humans correctly distinguished GPT-3 generated texts from real human texts approximately 52% of the time, which is not significantly higher than a random chance [5]. For more details on GPT-3 and other related topics we direct the reader to, for example, a recent survey [23].…”

Section: Profile and Nlp Featuresmentioning

confidence: 99%

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Detecting Bots in Social-Networks Using Node and Structural Embeddings

Dehghan

Siuta

Skorupka

et al. 2022

Preprint

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Users on social networks such as Twitter interact with and are influenced by each other without much knowledge of the identity behind each user. This anonymity has created a perfect environment for bot and hostile accounts to influence the network by mimicking real-user behaviour. To combat this, research into designing algorithms and datasets for identifying bot users has gained significant attention. In this work, we highlight various techniques for classifying bots, focusing on the use of node and structural embedding algorithms. We show that embeddings can be used as unsupervised techniques for building features with predictive power for identifying bots. By comparing features extracted from embeddings to other techniques such as NLP, user profile and node-features, we demonstrate that embeddings can be used as unique source of predictive information. Finally, we study the stability of features extracted using embeddings for tasks such as bot classification by artificially introducing noise in the network. Degradation of classification accuracy is comparable to models trained on carefully designed node features, hinting at the stability of embeddings.

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Section: Profile and Nlp Featuresmentioning

confidence: 99%

“…For this reason there is an increasing need for effective methods of analysis data represented as graphs. For more details we direct the reader to a recent survey [23] and a book [21].…”

Section: Introductionmentioning

confidence: 99%