86 research outputs found
Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos
What makes content go viral? Which videos become popular and why others
don't? Such questions have elicited significant attention from both researchers
and industry, particularly in the context of online media. A range of models
have been recently proposed to explain and predict popularity; however, there
is a short supply of practical tools, accessible for regular users, that
leverage these theoretical results. HIPie -- an interactive visualization
system -- is created to fill this gap, by enabling users to reason about the
virality and the popularity of online videos. It retrieves the metadata and the
past popularity series of Youtube videos, it employs Hawkes Intensity Process,
a state-of-the-art online popularity model for explaining and predicting video
popularity, and it presents videos comparatively in a series of interactive
plots. This system will help both content consumers and content producers in a
range of data-driven inquiries, such as to comparatively analyze videos and
channels, to explain and predict future popularity, to identify viral videos,
and to estimate response to online promotion.Comment: 4 page
Shallow reading with Deep Learning: Predicting popularity of online content using only its title
With the ever decreasing attention span of contemporary Internet users, the
title of online content (such as a news article or video) can be a major factor
in determining its popularity. To take advantage of this phenomenon, we propose
a new method based on a bidirectional Long Short-Term Memory (LSTM) neural
network designed to predict the popularity of online content using only its
title. We evaluate the proposed architecture on two distinct datasets of news
articles and news videos distributed in social media that contain over 40,000
samples in total. On those datasets, our approach improves the performance over
traditional shallow approaches by a margin of 15%. Additionally, we show that
using pre-trained word vectors in the embedding layer improves the results of
LSTM models, especially when the training set is small. To our knowledge, this
is the first attempt of applying popularity prediction using only textual
information from the title
Recurrent Neural Networks for Online Video Popularity Prediction
In this paper, we address the problem of popularity prediction of online
videos shared in social media. We prove that this challenging task can be
approached using recently proposed deep neural network architectures. We cast
the popularity prediction problem as a classification task and we aim to solve
it using only visual cues extracted from videos. To that end, we propose a new
method based on a Long-term Recurrent Convolutional Network (LRCN) that
incorporates the sequentiality of the information in the model. Results
obtained on a dataset of over 37'000 videos published on Facebook show that
using our method leads to over 30% improvement in prediction performance over
the traditional shallow approaches and can provide valuable insights for
content creators
Scalable Privacy-Compliant Virality Prediction on Twitter
The digital town hall of Twitter becomes a preferred medium of communication
for individuals and organizations across the globe. Some of them reach
audiences of millions, while others struggle to get noticed. Given the impact
of social media, the question remains more relevant than ever: how to model the
dynamics of attention in Twitter. Researchers around the world turn to machine
learning to predict the most influential tweets and authors, navigating the
volume, velocity, and variety of social big data, with many compromises. In
this paper, we revisit content popularity prediction on Twitter. We argue that
strict alignment of data acquisition, storage and analysis algorithms is
necessary to avoid the common trade-offs between scalability, accuracy and
privacy compliance. We propose a new framework for the rapid acquisition of
large-scale datasets, high accuracy supervisory signal and multilanguage
sentiment prediction while respecting every privacy request applicable. We then
apply a novel gradient boosting framework to achieve state-of-the-art results
in virality ranking, already before including tweet's visual or propagation
features. Our Gradient Boosted Regression Tree is the first to offer
explainable, strong ranking performance on benchmark datasets. Since the
analysis focused on features available early, the model is immediately
applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective
Content Analysi
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