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Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated content on these platforms, ranging from eyewitness accounts to multimedia evidence, serves as invaluable resources for corroborating and contextualizing cyber attacks, facilitating the attribution of malicious actors. Furthermore, social media data afford unique access to public sentiment, the propagation of propaganda, and emerging narratives, offering profound insights into the effectiveness of information operations and shaping counter-messaging strategies. However, there have been hardly any studies reported on the Russia–Ukraine cyber war harnessing social media analytics. This paper presents a comprehensive analysis of the crucial role of social-media-based cyber intelligence in understanding Russia's cyber threats during the ongoing Russo–Ukrainian conflict. This paper introduces an innovative multidimensional cyber intelligence framework and utilizes Twitter data to generate cyber intelligence reports. By leveraging advanced monitoring tools and NLP algorithms, like language detection, translation, sentiment analysis, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), Porter stemming, n-grams, and others, this study automatically generated cyber intelligence for Russia and Ukraine. Using 37,386 tweets originating from 30,706 users in 54 languages from 13 October 2022 to 6 April 2023, this paper reported the first detailed multilingual analysis on the Russia–Ukraine cyber crisis in four cyber dimensions (geopolitical and socioeconomic; targeted victim; psychological and societal; and national priority and concerns). It also highlights challenges faced in harnessing reliable social-media-based cyber intelligence. [ABSTRACT FROM AUTHOR]

With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combined for joint topic-sentiment modeling which yields semantic topics associated with sentiments. Recent breakthroughs in natural language processing have also not been leveraged for joint topic-sentiment modeling so far. Inspired by these advancements, this paper presents a novel framework for joint topic-sentiment modeling of short texts based on pre-trained language models and a clustering approach. The method leverages techniques from dimensionality reduction and clustering for which multiple algorithms were considered. All configurations were experimentally compared against existing joint topic-sentiment models and an independent sequential baseline. Our framework produced clusters with semantic topic quality scores of up to 0.23 while the best score among the previous approaches was 0.12 . The sentiment classification accuracy increased from 0.35 to 0.72 and the uniformity of sentiments within the clusters reached up to 0.9 in contrast to the baseline of 0.56 . The presented approach can benefit various research areas such as disaster management where sentiments associated with topics can provide practical useful information. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies. [ABSTRACT FROM AUTHOR]

With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around 15 , 000 tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of ≈78% using standard machine learning and ≈81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data. [ABSTRACT FROM AUTHOR]