YouTube | EPFL Graph Search

YouTube, LLC is an American online video sharing and social media platform headquartered in San Bruno, California, United States. Accessible worldwide, it was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users, who collectively watch more than one billion hours of videos each day. , videos were being uploaded at a rate of more than 500 hours of content per minute. In October 2006, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube also approved creators to participate in Google's AdSense program, which seeks to generate more

Essays in Empirical Asset Pricing

Alexis Arilès Marchal

This thesis consists of three applications of machine learning techniques to empirical asset pricing.In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short-term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.In the second part, which is co-authored work with Oksana Bashchenko, we develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.In the third part, I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This chapter can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.

EPFL2022

The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

Joan Isaac Biel Tres, Daniel Gatica-Perez

Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.

Ieee-Inst Electrical Electronics Engineers Inc2013

VlogSense: Conversational Behavior and Social Attention in YouTube

Joan Isaac Biel Tres, Daniel Gatica-Perez

We introduce the automatic analysis of conversational vlogs (VlogSense, for short) as a new research domain in social media. Conversational vlogs are inherently multimodal, depict natural behavior, and are suitable for large-scale analysis. Given their diversity in terms of content, VlogSense requires the integration of robust methods for multimodal analysis and for social media understanding. We present an original study on the automatic characterization of vloggers' audiovisual nonverbal behavior, grounded in work from social psychology and behavioral computing. Our study on 2,269 vlogs from YouTube shows that several nonverbal cues are significantly correlated with the social attention received by videos.

2011

The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

Joan Isaac Biel Tres, Daniel Gatica-Perez

Ieee-Inst Electrical Electronics Engineers Inc2013

Essays in Empirical Asset Pricing

Alexis Arilès Marchal

EPFL2022