Information spreading on social media contributes to the formation of collective opinions. Millions of social media users are exposed every day to popular memes -- some generated organically by grassroots activity, others sustained by advertising, information campaigns or more or less transparent coordinated efforts. While most information campaigns are benign, some may have nefarious purposes, including terrorist propaganda, political astroturf, and financial market manipulation. This poses a crucial technological challenge with deep social implications: can we detect whether the spreading of a viral meme is being sustained by a promotional campaign? Here we study trending memes that attract attention either organically, or by means of advertisement. We designed a machine learning framework capable to detect promoted campaigns and separate them from organic ones in their early stages. Using a dataset of millions of posts associated with trending Twitter hashtags, we prove that remarkably accurate early detection is possible, achieving 95% AUC score. Feature selection analysis reveals that network diffusion patterns and content cues are powerful early detection signals.

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.

Creating a musical fitness function is largely subjective and can be critically affected by the designer's biases. Previous attempts to create such functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also are limited to producing music strictly in the style determined by the programmer. We show in this paper that musical feature extractors, which avoid the challenges of qualitative judgment, enable creation of a multi-objective function for direct music production. The main result is that the multi-objective fitness function enables creation of music with varying identifiable styles. To demonstrate this, we use three different multi-objective fitness functions to create three distinct sets of musical melodies. We then evaluate the distinctness of these sets using three different approaches: a set of traditional computational clustering metrics; a survey of non-musicians; and analysis by three trained musicians.

We present a novel, real-time system for exploring harmonic spaces of musical styles, to generate music in collaboration with human performers utilizing gesture devices (such as the Kinect) together with MIDI and OSC instruments / controllers. This corpus-based environment incorporates statistical and evolutionary components for exploring potential flows through harmonic spaces, utilizing power-law (Zipf-based) metrics for fitness evaluation. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the Classical Music Archives 14,000 MIDI corpus, among others. The user interface supports real-time exploration of the balance between predictability and surprise for musical composition and performance, and may be used in a variety of musical contexts and applications.

"Food opera" is the term that the authors have applied to a new genre of audio-gustatory experience, in which a multi-course meal is paired with real-time, algorithmically generated music, deployed over a massively multichannel sound system. This paper presents an overview of the system used to deploy the sonic component of these events, while also exploring the history and creative potential of this unique multisensory format.

Hidden Markov Models have been used frequently in the audio domain to identify underlying musical structure. Much less work has been done in the purely symbolic realm. Recently, a substantial amount of expert-labelled symbolic musical data has been injected into the research community. The new availability of data allows for the application of machine learning models to purely symbolic tasks. Similarly, the continued expansion of the field of machine learning provides new perspectives and implementations of machine learning methods, which are powerful tools when approaching complex musical challenges. This research explores the use of an extended probabilistic model such as the Hidden Semi-Markov Model (HSMM) to approach the task of automatic harmonization. One distinct advantage of the HSMM is that it is able to automatically differentiate harmonic boundaries, through its inclusion of an extra parameter: duration. In this way, a melody can be harmonized automatically in the style of a particular corpus. In the case of this research, the corpus was in the style of Rock'n' Roll.

HARMI (Human and Robotic Musical Improvisation) is a software and hardware system that enables musical robots to improvise with human performers. The goal of the system is not to replicate human musicians, but rather to explore the novel kinds of musical expression that machines can produce. At the same time, the system seeks to create spaces where humans and robots can communicate with each other in a common language. To help achieve the former, ideas from contemporary compositional practice and music theory were used to shape the system's expressive capabilities. In regard to the latter, research from the field of cognitive psychology was incorporated to enable communication, interaction, and understanding between human and robotic performers. The system was partly developed in conjunction with a residency at High Concept Laboratories in Chicago, IL, where a group of human improvisers performed with the robotic instruments. The system represents an approach to the question of how humans and robots can interact and improvise in musical contexts. This approach purports to highlight the unique expressive spaces of humans, the unique expressive spaces of machines, and the shared spaces between the two.

The computer game industry has become a multibillion-dollar commercial enterprise, comparable in size and scope to the film industry. Game system requirements are an important driver of hardware and so ware innovation in the computer industry, and games have expanded to fill market niches opened by new platforms such as mobile phones, consoles, tablets, and social media. Artificial intelligence is a major component contributing to this success, creating the conditions for more complex virtual environments, realistic non-player characters, and engaging experiences.

Players in Dear Leader's Happy Story Time are placed in the role of contestants in a reality TV show where they are forced to audition for roles in the upcoming film of the host, a deranged billionaire who has inexplicably been elected president. The stories are produced by a story generator that combines stock plots and characters to produce kitsch story outlines. The players then collaborate to improvise a camp performance of the outline. The game design provides a context for experimenting with automatic story generation within a narrative game, as well as an opportunity for experimenting with knowledge representation schemes for expressing the tropes of popular narrative. The story generator uses a higher-order logic for describing tropes, and an HTN planning algorithm based on Nau et al.'s SHOP.

Apple Inc. has acquired a startup called AI Music that uses artificial intelligence to generate tailor-made music, according to a person with knowledge of the matter, adding technology that could be used across its slate of audio offerings. The purchase of AI Music, a London-based business founded in 2016, was completed in recent weeks. The company had about two dozen employees before the deal. Technology developed by AI Music can create soundtracks using royalty-free music and artificial intelligence, according to a copy of its now-defunct website. The idea is to generate dynamic soundtracks that change based on user interaction.