6,042 research outputs found
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
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
Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking
Public speaking is an important aspect of human communication and
interaction. The majority of computational work on public speaking concentrates
on analyzing the spoken content, and the verbal behavior of the speakers. While
the success of public speaking largely depends on the content of the talk, and
the verbal behavior, non-verbal (visual) cues, such as gestures and physical
appearance also play a significant role. This paper investigates the importance
of visual cues by estimating their contribution towards predicting the
popularity of a public lecture. For this purpose, we constructed a large
database of more than TED talk videos. As a measure of popularity of the
TED talks, we leverage the corresponding (online) viewers' ratings from
YouTube. Visual cues related to facial and physical appearance, facial
expressions, and pose variations are extracted from the video frames using
convolutional neural network (CNN) models. Thereafter, an attention-based long
short-term memory (LSTM) network is proposed to predict the video popularity
from the sequence of visual features. The proposed network achieves
state-of-the-art prediction accuracy indicating that visual cues alone contain
highly predictive information about the popularity of a talk. Furthermore, our
network learns a human-like attention mechanism, which is particularly useful
for interpretability, i.e. how attention varies with time, and across different
visual cues by indicating their relative importance
- …