Bots In Social Networks Research Articles

Approach to Detecting Malicious Bots in the Vkontakte Social Network and Assessing Their Parameters

The emergence of new varieties of bots in social networks and the improvement of their capabilities to imitate the natural behavior of real users represent a significant problem in the field of protection of social networks and online communities. This paper proposes a new approach to detecting and assessing the parameters of bots within the social network «VKontakte». The basis of the proposed approach is the creation of datasets using the method of «controlled purchase» of bots, which allows one to assess bots’ characteristics such as price, quality, and speed of action of bots, and using the Turing Test to assess how much users trust bots. In combination with traditional machine learning methods and features extracted from interaction graphs, text messages, and statistical distributions, it becomes possible to not only detect bots accurately but also predict their characteristics. This paper demonstrates that the trained machine learning model, based on the proposed approach, is robust to imbalanced data and can identify most types of bots as it has only a minor correlation with their main characteristics. The proposed approach can be used within the choice of countermeasures for the protection of social networks and for historical analysis, which allows not only to confirm the presence of bots but also to characterize the specifics of the attack.

Computer propaganda is a new trend in communication technologies

The article analyzes in detail the activity of one of the communication technologies – computer propaganda. As technology develops, the types and forms of communication technologies used in political and social processes change. The article presents a comparative analysis of the features of modern computer propaganda and traditional propaganda, based on foreign research works. The work of political bots in social networks that implement propaganda has also been studied. It provides tan analysis of the rate of actions of computer propaganda in political events. Also, it explains the problems of the automatic implementation of promotion algorithms in social networks, such as Twitter and Facebook.

Research on the generalization of social bot detection from two dimensions: feature extraction and detection approaches

PurposeThe proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.Design/methodology/approachThis paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.FindingsThe experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.Originality/valueBased on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.

Detecting Bots and Assessing Their Impact in Social Networks

Bots Impact Opinions in Social Networks: Let’s Measure How Much There is a serious threat posed by bots that try to manipulate opinions in social networks. In “Assessing the Impact of Bots on Social Networks,” Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouiji, and Tauhid Zaman present a new set of operational capabilities to detect these bots and measure their impact. They developed an algorithm based on the Ising model from statistical physics to find coordinating gangs of bots in social networks. They then created an algorithm based on opinion dynamics models to quantify the impact that bots have on opinions in a social network. They applied their algorithms to a variety of real social network data sets. They found that, for topics such as Brexit, the bots had little impact, whereas for topics such as the U.S. presidential debate and the Gilets Jaunes protests in France, the bots had a significant impact.

Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).

Identification of Bots in Social Networks based on Data Mining Technologies

Identification of Bots in Social Networks based on Data Mining Technologies

Identifying Bots in Social Networks Using the Example of LiveJournal

Social networks have firmly entered the lives of billions of global Internet users worldwide. They communicate in social networks, play online games, make purchases, organise online events — exchange content from all walks of life [1, 2]. The most popular and well-known services in Russia are Vkontakte (vk.com), Youtube.com, Facebook.com, Odnoklassniki (Ok.ru), etc. The interfaces of such platforms allo — fake accounts. In this paper, we propose an approach to detect bots using the LiveJournal social network as an example. For this, we investigated the characteristics of the user’s egograph and performed a comparative analysis of the results of the classification algorithms.

An approach to identifying bots in social networks based on the special association of classifiers

Currently the use of bots, i.e., auto-accounts in social networks which are managed with special programs but disguised as ordinary users, has serious consequences. For example, bots have been used to influence political elections, distort information on the Internet and manipulate prices on the stock exchange. Many research teams concerned with the detection of such accounts have made use of machine learning methods. However, the practical results of detecting social network bots indicate significant limitations because the methodological tools used have language limitation and ineffective criteria for detection. This article presents improved countermeasures in a methodological approach to develop a universal social network account classifier for minimizing the average risk of errors in bot detection. The application of an assembly of classifiers united by a data adaptation criterion and results from the variance of each model found the formation of a universal classifier for social network accounts. The main results obtained by the authors consist of the criteria system and the categorical (nominal) features transformation approach for the formation of the special ensemble of classifiers. In practice, use of the ensemble of classifiers allows us to increase the effectiveness of bot detection compared to other approaches.

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

The detection and removal of malicious social bots in social networks has become an area of interest in industry and academia. The widely used bot detection method based on machine learning leads to an imbalance in the number of samples in different categories. Classifier bias leads to a low detection rate of minority samples. Therefore, we propose an improved conditional generative adversarial network (improved CGAN) to extend imbalanced data sets before applying training classifiers to improve the detection accuracy of social bots. To generate an auxiliary condition, we propose a modified clustering algorithm, namely, the Gaussian kernel density peak clustering algorithm (GKDPCA), which avoids the generation of data-augmentation noise and eliminates imbalances between and within social bot class distributions. Furthermore, we improve the CGAN convergence judgment condition by introducing the Wasserstein distance with a gradient penalty, which addresses the model collapse and gradient disappearance in the traditional CGAN. Three common oversampling algorithms are compared in experiments. The effects of the imbalance degree and the expansion ratio of the original data on oversampling are studied, and the improved CGAN performs better than the others. Experimental results comparing with three common oversampling algorithms show that the improved CGAN achieves the higher evaluation scores in terms of F1-score, G-mean and AUC.

Обнаружение ботов в социальных сетях с помощью многослойного перцептрона

Обнаружение ботов в социальных сетях с помощью многослойного перцептрона

The use of an artificial neural network to detect automatically managed accounts in social networks

The problem of detection of automatically managed accounts (bots) in social networks has been considered. The method of their detection based on machine learning methods is proposed. The paper describes an example of a method based on artificial neural network learning. The parameters of a page in a social network used to detect bots have been presented. An experimental evaluation of the proposed system performance is given that demonstrates a high level of detection of bots in social networks.