Cannabidiol tweet miner: a framework for identifying misinformation In CBD tweets.

As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and drug surveillance. By leveraging Twitter data, NLP can offer invaluable insights into public perceptions around CBD, as well as the marketing tactics employed by those marketing such loosely-regulated substances to the general public. Given the lack of comprehensive clinical CBD testing, the various health claims made by CBD sellers regarding their products are highly dubious and potentially perilous, as is evident from the ongoing COVID-19 misinformation. It is therefore critically important to efficiently identify unsupportable claims to guide public health policy and action. To this end, we present our proposed framework, the Cannabidiol Tweet Miner (CBD-TM), which utilizes advanced natural language processing (NLP) techniques, including text mining and sentiment analysis, to analyze the similarities and differences between commercial and personal tweets that mention CBD. CBD-TM enables us to identify conditions typically associated with commercial CBD advertising, or conditions not associated with positive sentiment, that are also absent from personal conversations. Through our technical contributions, including NLP, text mining, and sentiment analysis, we can effectively uncover areas where the public may be misled by CBD sellers. Since the rise in popularity of CBD, advertisements making bold claims about its benefits have become increasingly prevalent. The COVID-19 pandemic created a new opportunity for sellers to promote and sell products that purportedly treat and/or prevent the virus, with CBD being one of them. Although the U.S. Food and Drug Administration issued multiple warnings to CBD sellers, this type of misinformation still persists. In response, we have extended the CBD-TM framework with an additional layer of tweet classification designed to identify tweets that make potentially misleading claims about CBD\u27s efficacy in treating and/or preventing COVID-19. Our approach harnesses modern NLP algorithms, utilizing a transformer-based language model to establish the semantic relationship between statements extracted from the FDA\u27s website that contain false information and tweets conveying similar false claims. Our technical contributions build upon the impressive performance of deep language models in various natural language processing and understanding tasks. Specifically, we employ transfer learning via pre-trained deep language models, enabling us to achieve improved misinformation identification in tweets, even with relatively small training sets. Furthermore, this extension of CBD-TM can be easily adapted to detect other forms of misinformation. Through our innovative use of NLP techniques and algorithms, we can more effectively identify and combat false and potentially harmful claims related to CBD and COVID-19, as well as other forms of misinformation. As the conversations surrounding CBD on Twitter evolve over time, concept drift can occur, leading to changes in the topics being discussed. We observed significant changes within the CBD Twitter data stream with the emergence of COVID-19, introducing a new medical condition associated with CBD that would not have been discussed in conversations prior to the pandemic. These shifts in conversation introduce concept drift into CBD-TM, which has the potential to negatively impact our tweet classification models. Therefore, it is crucial to identify when such concept drift occurs to maintain the accuracy of our models. To this end, we propose an innovative approach for identifying potential changes within social network streams, allowing us to determine how and when these conversations evolve over time. Our approach leverages a BERT-based topic model, which can effectively capture how conversations related to CBD change over time. By incorporating advanced NLP techniques and algorithms, we are able to better understand the changes in topic that occur within the CBD Twitter data stream, allowing us to more effectively manage concept drift in CBD-TM. Our technical contributions enable us to maintain the accuracy and effectiveness of our tweet classification models, ensuring that we can continue to identify and address potentially harmful misinformation related to CBD

Concept Drift Adaptation in Text Stream Mining Settings: A Comprehensive Review

Due to the advent and increase in the popularity of the Internet, people have been producing and disseminating textual data in several ways, such as reviews, social media posts, and news articles. As a result, numerous researchers have been working on discovering patterns in textual data, especially because social media posts function as social sensors, indicating peoples' opinions, interests, etc. However, most tasks regarding natural language processing are addressed using traditional machine learning methods and static datasets. This setting can lead to several problems, such as an outdated dataset, which may not correspond to reality, and an outdated model, which has its performance degrading over time. Concept drift is another aspect that emphasizes these issues, which corresponds to data distribution and pattern changes. In a text stream scenario, it is even more challenging due to its characteristics, such as the high speed and data arriving sequentially. In addition, models for this type of scenario must adhere to the constraints mentioned above while learning from the stream by storing texts for a limited time and consuming low memory. In this study, we performed a systematic literature review regarding concept drift adaptation in text stream scenarios. Considering well-defined criteria, we selected 40 papers to unravel aspects such as text drift categories, types of text drift detection, model update mechanism, the addressed stream mining tasks, types of text representations, and text representation update mechanism. In addition, we discussed drift visualization and simulation and listed real-world datasets used in the selected papers. Therefore, this paper comprehensively reviews the concept drift adaptation in text stream mining scenarios.Comment: 49 page

Toxicity in Evolving Twitter Topics - Employing a novel Dynamic Topic volution Model (DyTEM) onTwitter data

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis thesis presents an extensive investigation into the evolution of topics and their association with speech toxicity on Twitter, based on a large corpus of tweets, providing crucial insights for monitoring online discourse and potentially informing interventions to combat toxic behavior in digital communities. A Dynamic Topic Evolution Model (DyTEM) is introduced, constructed by combining static Topic Modelling techniques and sentence embeddings through the state-of-the-art sentence transformer, sBERT. The DyTEM, tested and validated on a substantial sample of tweets, is represented as a directed graph, encapsulating the inherent dynamism of Twitter discussions. For validating the consistency of DyTEM and providing guidance for hyperparameter selection, a novel, hashtag-based validation method is proposed. The analysis identifies and scrutinizes five distinct Topic Transition Types: Topic Stagnation, Topic Merge, Topic Split, Topic Disappearance, and Topic Emergence. A speech toxicity classification model is employed to delve into the toxicity dynamics within topic evolution. A standout finding of this study is the positive correlation between topic popularity and its toxicity, implying that trending or viral topics tend to contain more inflammatory speech. This insight, along with the methodologies introduced in this study, contributes significantly to the broader understanding of digital discourse dynamics and could guide future strategies aimed at fostering healthier and more constructive online spaces

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses

Ensemble deep learning: A review

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions