Twitter and social bots : an analysis of the 2021 Canadian election

Les médias sociaux sont désormais des outils de communication incontournables, notamment lors de campagnes électorales. La prévalence de l’utilisation de plateformes de communication en ligne suscite néanmoins des inquiétudes au sein des démocraties occidentales quant aux risques de manipulation des électeurs, notamment par le biais de robots sociaux. Les robots sociaux sont des comptes automatisés qui peuvent être utilisés pour produire ou amplifier le contenu en ligne tout en se faisant passer pour de réels utilisateurs. Certaines études, principalement axées sur le cas des États-Unis, ont analysé la propagation de contenus de désinformation par les robots sociaux en période électorale, alors que d’autres ont également examiné le rôle de l’affiliation partisane sur les comportements et les tactiques favorisées par les robots sociaux. Toutefois, la question à savoir si l'orientation partisane des robots sociaux a un impact sur la quantité de désinformation politique qu’ils propagent demeure sans réponse. Par conséquent, l’objectif principal de ce travail de recherche est de déterminer si des différences partisanes peuvent être observées dans (i) le nombre de robots sociaux actifs pendant la campagne électorale canadienne de 2021, (ii) leurs interactions avec les comptes réels, et (iii) la quantité de contenu de désinformation qu’ils ont propagé. Afin d’atteindre cet objectif de recherche, ce mémoire de maîtrise s’appuie sur un ensemble de données Twitter de plus de 11,3 millions de tweets en anglais provenant d’environ 1,1 million d'utilisateurs distincts, ainsi que sur divers modèles pour distinguer les comptes de robots sociaux des comptes humains, déterminer l’orientation partisane des utilisateurs et détecter le contenu de désinformation politique véhiculé. Les résultats de ces méthodes distinctes indiquent des différences limitées dans le comportement des robots sociaux lors des dernières élections fédérales. Il a tout de même été possible d'observer que les robots sociaux de tendance conservatrice étaient plus nombreux que leurs homologues de tendance libérale, mais que les robots sociaux d’orientation libérale étaient ceux qui ont interagi le plus avec les comptes authentiques par le biais de retweets et de réponses directes, et qui ont propagé le plus de contenu de désinformation.Social media have now become essential communication tools, including within the context of electoral campaigns. However, the prevalence of online communication platforms has raised concerns in Western democracies about the risks of voter manipulation, particularly through social bot accounts. Social bots are automated computer algorithms which can be used to produce or amplify online content while posing as authentic users. Some studies, mostly focused on the case of the United States, analyzed the propagation of disinformation content by social bots during electoral periods, while others have also examined the role of partisanship on social bots’ behaviors and activities. However, the question of whether social bots’ partisan-leaning impacts the amount of political disinformation content they generate online remains unanswered. Therefore, the main goal of this study is to determine whether partisan differences could be observed in (i) the number of active social bots during the 2021 Canadian election campaign, (ii) their interactions with humans, and (iii) the amount of disinformation content they propagated. In order to reach this research objective, this master’s thesis relies on an original Twitter dataset of more than 11.3 million English tweets from roughly 1.1 million distinct users, as well as diverse models to distinguish between social bot and human accounts, determine the partisan-leaning of users, and detect political disinformation content. Based on these distinct methods, the results indicate limited differences in the behavior of social bots in the 2021 federal election. It was however possible to observe that conservative-leaning social bots were more numerous than their liberal-leaning counterparts, but liberal-leaning accounts were those who interacted more with authentic accounts through retweets and replies and shared the most disinformation content

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Social Media Behaviour Analysis in Disaster-Response Messages of Floods and Heat Waves via Artificial Intelligence

This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform a qualitative analysis via topic inference in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with 7 additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection

Cybersecurity and safety analysis in online social networks

The research work deal with the security and safety issues related to the use of online social networks and it successfully presented AI-based solutions to address these issues in online social networks

An improved bees algorithm local search mechanism for numerical dataset

Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Fire induced Progressive Collapse Potential assessment of Steel Framed Buildings using machine learning

In this paper, a new Machine Learning framework is developed for fast prediction of the failure patterns of simple steel framed buildings in fire and subsequent progressive collapse potential assessment. This pilot study provides a new tool of fire safety assessment for engineers in an efficient and effective way in the future. The concept of Critical Temperature Method is used to define the failure patterns for each structural member which is incorporated into a systematic methodology employing both Monte Carlo Simulation and Random Sampling to generate a robust and sufficient large dataset for training and testing, hence guarantees the accurate prediction. A comparative study for different machine learning classifiers is made. Three classifiers are chosen for failure patterns prediction of buildings under fire: Decision Tree, KNN and Neural Network using Google Keras with TensorFlow which is specially used for Google Brain Team. The Machine Learning framework is implemented using codes programmed by the author in VBA and Python language. A case study of a 2 story by 2 bay steel framed building was made. Two different fire scenarios were chosen. The procedure gives satisfactory prediction of the failure pattern and collapse potential of the building under fire