Identification of credulous users on Twitter

Balestrucci, Alessandro; Nicola, Rocco De; Inverso, Omar; Trubiani, Catia

doi:10.1145/3297280.3297486

Cited by 16 publications

(25 citation statements)

References 33 publications

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“…Moreover, some of these datasets were already used to train and evaluate the so-called Botometer (formerly BotOrNot) service proposed by [12]. However, as authors described in [7], [28], the generation of bot accounts continuously changes over time and additionally, some of the provided accounts have been already suspended by Twitter. Thus, a preprocessing is needed to improve the usability of this large collection of datasets by removing the identifiers those accounts that were already removed by Twitter.…”

Section: ) Dataset Generationmentioning

confidence: 99%

“…[11] model. In previous related studies [7], [12], [13], [20], the proposed solutions had two main constraints: 1) either they were metadata-oriented approaches so that, the text-based features were extracted at a semantic-level in terms of Natural Language Processing, or 2) they employed more advanced NLP procedures based on N-grams or DL solutions but they only supported a limited number of languages when performing the analysis. However, our proposal addresses the aforementioned constraints by combining relevant metadata features along with powerful models capable of transforming text-based features into vectors independently of the language of the input text.…”

Section: Political-bots-2019mentioning

confidence: 99%

“…The outcomes obtained during the training and validation stages for all the possible input feature vectors are summarized in Table 3. Since the dataset is not balanced, the F1score metric has a crucial role in the evaluation of the system in order to objective measure the performance of identifying bots in a social network such as Twitter where only a few accounts from the total set of accounts belong indeed to the bot category as it is described in previous studies [7], [27], [34]. In particular, since the model is trained using an Early-Stopping callback to stop the process in the epoch when the loss function in the validation set is no longer decreasing, the F1-score was computed by using both the recall and the precision at this specific epoch.…”

Section: A Training and Validating Bot-densenetmentioning

confidence: 99%

See 2 more Smart Citations

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

et al. 2021

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During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied research to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadata information or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framework.

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Section: ) Dataset Generationmentioning

confidence: 99%

Section: Political-bots-2019mentioning

confidence: 99%

Section: A Training and Validating Bot-densenetmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

et al. 2021

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“…Balestrucci et al [7] investigated the relationship between bots and legitimate human accounts. The authors sought to detect credulous accounts on Twitter starting with those involved in any social relationship with a bot, and proposed an approach to automatically rank them based on their gullibility.…”

Section: Sockpuppet Detectionmentioning

confidence: 99%

Social Media Identity Deception Detection: A Survey

Alharbi,

Dong,

et al. 2021

Preprint

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Social media have been growing rapidly and become essential elements of many people's lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey analyses various identity deception attacks, which can be categorized into fake profile, identity theft and identity cloning. This survey provides a detailed review of social media identity deception detection techniques. It also identifies primary research challenges and issues in the existing detection techniques. This article is expected to benefit both researchers and social media providers.CCS Concepts: • Security and privacy → Social engineering attacks; Social network security and privacy.

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“…In a previous work [3], the authors shed light on so called credulous Twitter users assuming, with a harmless abuse of language, that they refer to humanoperated accounts with a high percentage of bots as friends. Unlike [3], where the authors performed an analysis involving the friends of a set of human-operated accounts -a highly time consuming task -here we design and develop a classifier to find out credulous Twitter users, by considering a number of features that do not take the friendship with bots into account. Starting by considering a set of features commonly employed in the literature to detect bots [26,10], we end up with a lightweight classifier, in terms of costs for gathering the data needed for the feature engineering phase.…”

Section: Introductionmentioning

confidence: 99%

Do You Really Follow Them? Automatic Detection of Credulous Twitter Users

Balestrucci

Nicola

Petrocchi

et al. 2019

Lecture Notes in Computer Science

Self Cite

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Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users; even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. The best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results.

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Identification of credulous users on Twitter

Cited by 16 publications

References 33 publications

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

Social Media Identity Deception Detection: A Survey

Do You Really Follow Them? Automatic Detection of Credulous Twitter Users

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