Social Media Bots Research Articles

Comparison of Social Media BOT Functions Using the K-Nearest Neighbor Method Against User Satisfaction

Social media is currently an alternative medium in conveying messages in the form of news and can be used as a tool to exchange news from different places. Many people use social media to express opinions, express feelings, as well as experiences and things that can be of concern. In this study, the data processing used was the K-Nearest Neighbor Algorithm with the classification method as a media for comparing the functions of the two bots, namely the WhatsApp and Telegram bots. Using SPSS (Statistical Product and Serive Solutions) and Rapidminer as a place to perform calculations and analysis. Based on the results of testing data mining with Rapid Miner, the calculation results are obtained which will be used as information to support user satisfaction in using Social Media Bots. User satisfaction is found in WhatsApp Bot users 72.22% and Telegram Bot users 28.57%. Calculations are carried out with a data mining process obtained from the K-Nearest Neighbor algorithm to make it easier to find Bot user satisfaction on both WhatsApp and Telegram social media. The WhatsApp bot is the best choice and has several useful functions as a digital communication media tool on the Internet of Things.

Political social media bot detection: Unveiling cutting-edge feature selection and engineering strategies in machine learning model development

Over time, social media bots (SMBs), specifically political SMBs, have played a crucial role in influencing and spreading misinformation, manipulating public opinion, and harassing and intimidating users of online social networks (OSNs). This article aims to study previous works on the detection and analysis of political SMB activities and address critical challenges that significantly impact the effectiveness of SMB detection models. These challenges include feature engineering, feature selection (FS), and model implementation. Over 33 features were extracted from the Twibot-20 dataset, including content, user information, network, behavior, and temporal features. Various FS techniques are explored and compared to select the optimal features, comprising basic, filter, wrapper, embedded, and hybrid. The optimal features are then employed to train multiple machine-learning algorithms. To balance the dataset, the synthetic minority oversampling technique coupled with edited nearest neighbors (Smote-ENN) is used. The results showed an improvement in model performance, from an initial Area Under the Curve (AUC) of 90.40 % and accuracy of 81.60 % using the original set to a score of 99.50 % for the test set and 100 % for the training set in all used metrics. Decision Trees, Random Forest, Gradient Boosting, Adaboost, XGB, and Extra Trees emerge as the most effective for detecting political SMBs.

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.

Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets). We analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans. Over the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.

A Hybrid Deep Learning Architecture for Social Media Bots Detection Based on Bigru-LSTM and Glove Word Embedding

A Hybrid Deep Learning Architecture for Social Media Bots Detection Based on Bigru-LSTM and Glove Word Embedding

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to evasion in traditional feature engineering, and insufficient exploration of user relationships. To tackle these challenges, this paper proposes MRLBot, a social media bot detection framework based on unsupervised representation learning. We design a behavior representation learning model that utilizes Transformer and a CNN encoder–decoder to simultaneously extract global and local features from behavioral information. Furthermore, a network representation learning model is proposed that introduces intra- and outer-community-oriented random walks to learn structural features and community connections from the relationship graph. Finally, the behavioral representation and relationship representation learning models are combined to generate fused representations for bot detection. The experimental results of four publicly available social network datasets demonstrate that the proposed method has certain advantages over state-of-the-art detection methods in this field.

Using Social Media Bots to Keep up with a Vibrant Research Field: The Example of @MOF_papers

Using Social Media Bots to Keep up with a Vibrant Research Field: The Example of @MOF_papers

Political Bot Bias in the Perception of Online Discourse

Four nationally representative studies (N = 1,986; three preregistered) find evidence for a bias in how people perceive opposing viewpoints expressed through online discourse. These studies elucidate a political bot bias, where political partisans (vs. their out-party) are more likely to view counter-ideological (vs. ideologically consistent) tweets to be social media bots (vs. humans). Study 1 demonstrates that American Democrats and Republicans are more likely to attribute tweets to bots when those tweets express counter-ideological views. Study 2 demonstrated this bias with actual bot tweets generated by the Russian government and comparable human tweets. Study 3 demonstrated this bias manifests in the context of real recent elections and is associated with markers of political animosity. Study 4 experimentally demonstrates the consequences of bot attribution for perceptions of online political discourse. Our findings document a consistent bias that has implications for political discussion online and political polarization more broadly.

Examining the differences between human and bot social media accounts: A case study of the Russia-Ukraine War

In this study, we examined online conversations on Twitter about the Russia-Ukraine War and investigated differences between bots and non-bots accounts. Using ‘Russia’ and ‘Ukraine’ as keywords, we employed a Twitter API to collect data from 17 February to 18 March on Twitter. We obtained a large dataset of over 3.7 million tweets generated by about one million distinct accounts. We then analyzed one percent of the data using interval sampling for bot detection and found that about 13.4 percent of the accounts were social media bots, responsible for about 16.7 percent of the tweets. We examined the difference between bots and non-bots regarding online conversations on the Russia-Ukraine War through account analysis, textual analysis, and interaction analysis. The results show that bots exist on both sides, bots from the Ukrainian side contributed a louder voice while bots on the Russian side demonstrated more effective communication. In addition, there were differences and similarities between bots and non-bots in the behavior of online conversations, but the difference seemed to be relatively weaker than those found in previous studies.

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism.

BackgroundSocial media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation.ObjectiveThis study aimed to study the role of social media bots during the COVID-19 outbreak.MethodsThe Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet.ResultsRegarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively.ConclusionsBy classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media.

Digital Propaganda: The Power of Influencers

Attempts to manipulate public opinion using social media and emerging information communication technologies (ICTs) continue to proliferate internationally. Governments, corporations, extremist groups, and a wide variety of other entities around the globe now commonly use both automated bots and anonymous human "sockpuppet" accounts in efforts to amplify and suppress particular streams of information during elections, security crises, and other pivotal events. They use these same tools to sow disinformation and engage in organized political trolling campaigns. However, the technologies and tactics used in these internet-based "influence operations" are changing. This essay leverages insights from over 70 interviews with people who both produce and track online manipulation campaigns. It compares emerging trends in digital disinformation and computational propaganda across the globe using qualitative data from 12 countries—Burma, Brazil, Egypt, Eritrea, Ethiopia, India, Indonesia, Mexico, the Philippines, Turkey, Ukraine, and the United States. In essence, internet-borne manipulation efforts are evolving from relatively unsophisticated "inorganic" campaigns pushed by social media bots and towards more complex "semi-organic" efforts combining both coordinated human users and artificial intelligence software. Additional, related, trends include the increased coercive political use of social media influencers and encrypted and private messaging applications.

A Socially Relevant Focused AI Curriculum Designed for Female High School Students

Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.

TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks

In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort ground realities and manipulate global trends, thus creating astroturfing attacks on the social media online portals. Moreover, these bots often tend to participate in duplicitous activities, including promotion of hidden agendas and indulgence in biased propagation meant for personal gain or scams. Thus, online bots have eventually become one of the biggest menaces for social media platforms. Therefore, we have proposed an AI-driven social media bot identification framework, namely TweezBot, which can identify fraudulent Twitter bots. The proposed bot detection method analyzes Twitter-specific user profiles having essential profile-centric features and several activity-centric characteristics. We have constructed a set of filtering criteria and devised an exhaustive bag of words for performing language-based processing. In order to substantiate our research, we have performed a comparative study of our model with the existing benchmark classifiers, such as Support Vector Machine, Categorical Naïve Bayes, Bernoulli Naïve Bayes, Multilayer Perceptron, Decision Trees, Random Forest and other automation identifiers.

Stabilizing a supervised bot detection algorithm: How much data is needed for consistent predictions?

Social media bots have been characterized in their use in digital activism and information manipulation, due to their roles in information diffusion. The detection of bots has been a major task within the field of social media computation, and many datasets and bot detection algorithms have been developed. With these algorithms, the bot score stability is key in estimating the impact of bots on the diffusion of information. Within several experiments on Twitter agents, we quantify the amount of data required for consistent bot predictions and analyze agent bot classification behavior. Through this study, we developed a methodology to establish parameters for stabilizing the bot probability score through threshold, temporal and volume analysis, eventually quantifying suitable threshold values for bot classification (i.e. whether the agent is a bot or not) and reasonable data collection size (i.e. number of days of tweets or number of tweets) for stable scores and bot classification.

Analyzing Political Polarization on Social Media by Deleting Bot Spamming

Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users.

Towards Immunizing Infodemic: Comprehensive Study on Assessing the Role of Artificial Intelligence and COVID-19 Pandemic

Artificial Intelligence (AI) technologies have intentionally and unintentionally been used to spread false information on all different types of subjects. Throughout the COVID-19 pandemic, there was a pool of different information that was being presented to the public, a lot of it contradicting one another. False information spreads regardless of whether there is intent to mislead or misinform whereas AI is not able to decipher what type of information it is pushing to the public is correct and what is not. This mass spread of information through online platforms has been coined as an Infodemic where it is considered a massive volume of information, both online and offline. It includes deliberate attempts to disseminate false information to undermine the public health response and advance alternative agendas of groups or individuals. An infodemic can be incredibly dangerous to society greatly affecting the ability of communities, societies, and countries to control and stop the pandemic due to the abundance of different information in combating the health crisis. This article assesses and evaluates the role of Artificial Intelligence (AI) technologies in helping to spread disinformation during the COVID-19 pandemic. It reviews and evaluates the information curation in modern media, the relationship between AI and disinformation, and the challenges of disinformation campaigns. It further outlines the impact of social media platforms on infodemic and their influence in spreading disinformation during the COVID-19 pandemic. This article analyzes several data mining studies that used different machine learning techniques to identify the influence of disinformation tactics on the COVID-19 pandemic associated with the Twitter platform. It further continues exploring the investigation of the number of influential tweets, the type of users, the levels of credibility of URLs, and the type and effect of social media bots. Finally, the authors assess and conclude how disinformation is widely prevalent throughout social media during the COVID-19 pandemic as well as illustrate the surveys that categorize the prevalence of users involved in the conversation about disinformation separated by country including the percentage of users posting tweets and retweeting news URLs, and the future work in combating the rapid disinformation campaigns and their ethical implication impact.

Artificial Intelligence in Communication Studies: An Investigation on Studies Between 1982-2021

This study explores the profile of media and communication studies on artificial intelligence and presents a perspective on the general structure of the literature. In this study, 459 scientific studies published between 1982 and 2021, which have been increasing even further since 2016, were examined via bibliometric data. The country that published the most was the USA with 25.1%. Turkey ranked 28th among 54 countries with four publications. Two hundred sixty-seven article publications were distributed in 96 different journals. By testing the compatibility of this distribution with Bradford’s Law, eight core journals meeting the needs of communication literature on artificial intelligence, were identified. New Media & Society ranked first on this list. Topics covered in the studies focused on journalism, natural language processing, human-robot interaction, social media bots, public relations and advertising. The most frequently used concepts in abstracts and keywords were “automated journalism,” “computational journalism,” “robot journalism,” “ethics,” “fake news.” The most frequently cited journal was Digital Journalism. The half-life value of cited publications was seven years. The results revealed the artificial intelligence trends in communication studies and the general structure of the communication literature. In addition, it is suggested that the results regarding literature obsolescence and core journals would contribute to the creation of subscriptions and journal collections in libraries.

Feature selection using Benford’s law to support detection of malicious social media bots

The increased amount of high-dimensional imbalanced data in online social networks challenges existing feature selection methods. Although feature selection methods such as principal component analysis (PCA) are effective for solving high-dimensional imbalanced data problems, they can be computationally expensive. Hence, an effortless approach for identifying meaningful features that are indicative of anomalous behaviour between humans and malicious bots is presented herein. The most recent Twitter dataset that encompasses the behaviour of various types of malicious bots (including fake followers, retweet spam, fake advertisements, and traditional spambots) is used to understand the behavioural traits of such bots. The approach is based on Benford’s law for predicting the frequency distribution of significant leading digits. This study demonstrates that features closely obey Benford’s law on a human dataset, whereas the same features violate Benford’s law on a malicious bot dataset. Finally, it is demonstrated that the features identified by Benford’s law are consistent with those identified via PCA and the ensemble random forest method on the same datasets. This study contributes to the intelligent detection of malicious bots such that their malicious activities, such as the dissemination of spam, can be minimised.

When Bots and Users Meet: Automated Manipulation and the New Culture of Online Suspicion

This article examines the growing public awareness of deceptive online campaigns where automated networks of social media bots are deployed to manipulate public opinion, disrupt debates, and stoke intercommunal strife. Drawing on a grounded thematic analysis of discussion threads that appeared on Israeli politicians’ Facebook pages during the lead-up to Israel’s national elections in April 2019, I argue that users’ frequent allusions and reactions to the presence of suspected manipulative agents allow these users to negotiate, challenge, and raise competing claims regarding whether certain prominent voices on social media are reflective of actual public opinion. As such, this article contributes to the emerging body of literature about online manipulation, which in recent years has focused mostly on examining the nature and scope of deceptive bot campaigns around the globe and on devising new techniques for detecting and countering the activities of fake social media accounts. The present investigation, in contrast, seeks to shift attention away from bots themselves and toward their intended targets: ordinary users and their discourse. In so doing, it aims to contribute to and expand the study of how automated manipulation is shaping contemporary social media environments by asking: What do ordinary users have to say about the deployment of political bots? How does users’ growing awareness of deceptive bot campaigns inform their interpretations of their own online experiences? And how do users leverage claims about bot activity in online exchanges with others to advance their political agendas?

Social Media Disinformation Bots May Harm Psychiatry

Social Media Disinformation Bots May Harm Psychiatry

Social Media Bots, Trolls, and the Democratic Mandates in Sub-Saharan Africa

This exploratory study investigates the role that bots and trolls played on social media in widening the gaps of political partisanship in sub-Saharan Africa. Taking the case of the 2018 Zambian by-elections, the authors examined the relationship between online social media content that propagates hate and organized trolling efforts in Zambia. The study used machine learning tools to identify the origin of the bots on Facebook and Twitter accounts (trolls) of the two major political parties in Zambia (PF and UPND). Online posts that accounted for the election campaigns and the aftermath in the year 2018 were considered for the study. Findings suggest that social-mediated conversations were divided along political lines and that the examined trolling accounts systematically took advantage of the existing echo chambers to create hate messages on Zambian social networks. In other words, the findings indicated that the online hate messages that accounted for violence were neither created by the PF or UPND political parties as earlier studies suggest but by bots and trolls.