This paper proposes a novel lexicon-based unsupervised sentimental analysis method to measure the $``\textit{hope}"$ and $``\textit{fear}"$ for the 2022 Ukrainian-Russian Conflict. $\textit{Reddit.com}$ is utilised as the main source of human reactions to daily events during nearly the first three months of the conflict. The top 50 $``hot"$ posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, UkraineWarReports) and their relative comments are scraped and a data set is created. On this corpus, multiple analyses such as (1) public interest, (2) hope/fear score, (3) stock price interaction are employed. We promote using a dictionary approach, which scores the hopefulness of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm of topic modelling is also utilised to understand the main issues raised by users and what are the key talking points. Experimental analysis shows that the hope strongly decreases after the symbolic and strategic losses of Azovstal (Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and negatives, are present after important battles, but also some non-military events, such as Eurovision and football games.

Toxic misinformation campaigns have caused significant societal harm, e.g., affecting elections and COVID-19 information awareness. Unfortunately, despite successes of (gold standard) retrospective studies of misinformation that confirmed their harmful effects after the fact, they arrive too late for timely intervention and reduction of such harm. By design, misinformation evades retrospective classifiers by exploiting two properties we call new-normal: (1) never-seen-before novelty that cause inescapable generalization challenges for previous classifiers, and (2) massive but short campaigns that end before they can be manually annotated for new classifier training. To tackle these challenges, we propose UFIT, which combines two techniques: semantic masking of strong signal keywords to reduce overfitting, and intra-proxy smoothness regularization of high-density regions in the latent space to improve reliability and maintain accuracy. Evaluation of UFIT on public new-normal misinformation data shows over 30% improvement over existing approaches on future (and unseen) campaigns. To the best of our knowledge, UFIT is the first successful effort to achieve such high level of generalization on new-normal misinformation data with minimal concession (1 to 5%) of accuracy compared to oracles trained with full knowledge of all campaigns.

In this study, we approached the Hit Song Prediction problem, which aims to predict which songs will become Billboard hits. We gathered a dataset of nearly 18500 hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four machine-learning models on our dataset. We were able to predict the Billboard success of a song with approximately 86\% accuracy. The most succesful algorithms were Random Forest and Support Vector Machine.

Classifieds provide many challenges for recommendation methods, due to the limited information regarding users and items. In this paper, we explore recommendation methods for classifieds using the example of OLX Jobs. The goal of the paper is to benchmark different recommendation methods for jobs classifieds in order to improve advertisements' conversion rate and user satisfaction. In our research, we implemented methods that are scalable and represent different approaches to recommendation, namely ALS, LightFM, Prod2Vec, RP3beta, and SLIM. We performed a laboratory comparison of methods with regard to accuracy, diversity, and scalability (memory and time consumption during training and in prediction). Online A/B tests were also carried out by sending millions of messages with recommendations to evaluate models in a real-world setting. In addition, we have published the dataset that we created for the needs of our research. To the best of our knowledge, this is the first dataset of this kind. The dataset contains 65,502,201 events performed on OLX Jobs by 3,295,942 users, who interacted with (displayed, replied to, or bookmarked) 185,395 job ads in two weeks of 2020. We demonstrate that RP3beta, SLIM, and ALS perform significantly better than Prod2Vec and LightFM when tested in a laboratory setting. Online A/B tests also demonstrated that sending messages with recommendations generated by the ALS and RP3beta models increases the number of users contacting advertisers. Additionally, RP3beta had a 20% greater impact on this metric than ALS.

This weekend, I succumbed to the pull of all the meme-y marketing and went to the theater to see the surprise horror-comedy hit M3gan. I generally enjoyed it--the jokes are funny, the jump scares effective, the robot-centric plot a rather smart addition to our fresh new wave of artificial intelligence anxiety. It isn't the goriest or most frightening flick--the blood streams had to stay PG-13--but the steadily paced tension and the references to horror classics do their job fine. Yet, to me, the most chilling aspect of the movie doesn't come from anything you might expect: the offscreen murders, M3gan's deranged humanoid face, the pressures of capitalism. It actually stems from a deceptively insignificant 10-second scene that comes about halfway through the movie, in which the titular bot takes to the house piano. To be clear, I don't find this scene so viscerally terrifying for the piano tune itself (in the film, a solid instrumental cover of Martika's 1989 No. 1 hit "Toy Soldiers"), or for the overall menace of the moment, a turning point in M3gan's development.

With the benefit of hindsight, it was always going to be television and not film where the first genuinely authentic video game tie-in would happen. The format of the ongoing drama series, with its capacity for multiple character and narrative arcs, as well as its extended running time, aligns much more closely with how games actually function. Even so, I am surprised by just how brilliant episode one of The Last of Us (HBO in the US, Sky Atlantic in the UK) is. It beautifully weaves the conventions of both TV and games into one gripping experience, using subjective camera shots to put us into the viewpoint of characters (like a game), while also toying with depth of field in a very televisual way to blur out background details for thrilling effect (oh god, the shaking granny!). Ever the optimist, I'm thinking that maybe – maybe – this series (pictured below) has unlocked a new set of multidisciplinary tools that will enable other TV and game makers to collaborate on exciting dramas.

This week, Dana and Julia are joined by Slate writer Dan Kois. They start by discussing HBO's new series, The Last of Us, a video game adaptation with culture editor and writer at The New Yorker, Alex Barasch. Then they discuss the French film, Saint Omer, shortlisted for Best International Film at the 2023 Oscars. Finally, they finish by talking about Dan's essay on how the Trunchbull, the formidable villain of Roald Dahl's 1988 novel, Matilda is still evolving. Dan: Two books publishing this week, an anti-romantic comedy, Really Good, Actually by Monica Heisey, about a young woman in Toronto failing to deal with her divorce, and a novel by Matthew Salesses titled The Sense of Wonder about the ways Asian Americans navigate the worlds of sports and entertainment when everything is stacked against them.

In my previous post, I discussed using ChatGPT, the large language model from OpenAI [1], as a writing partner for various types of prose. In this article, I will show how the system can be used to help compose music by generating chords from text prompts. After a brief overview of ChatGPT, I will show the results of my experiments in writing music with the new system in the following styles: jazz, country rock, and reggae. I'll finish by giving my general observations on using the model for composing music with some next steps for future exploration. Note that the third and final installment of this series will be about using the system to create picture books with help from Midjourney. ChatGPT is the latest language model from OpenAI that was designed and trained to interact with people via a chat user interface. GTP stands for Generative Pre-trained Transformer, where a transformer is a type of AI model. You can read a complete background on the system in this series' first article.

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model performance.

The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.