800 research outputs found
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers
Polarization in American politics has been extensively documented and
analyzed for decades, and the phenomenon became all the more apparent during
the 2016 presidential election, where Trump and Clinton depicted two radically
different pictures of America. Inspired by this gaping polarization and the
extensive utilization of Twitter during the 2016 presidential campaign, in this
paper we take the first step in measuring polarization in social media and we
attempt to predict individuals' Twitter following behavior through analyzing
ones' everyday tweets, profile images and posted pictures. As such, we treat
polarization as a classification problem and study to what extent Trump
followers and Clinton followers on Twitter can be distinguished, which in turn
serves as a metric of polarization in general. We apply LSTM to processing
tweet features and we extract visual features using the VGG neural network.
Integrating these two sets of features boosts the overall performance. We are
able to achieve an accuracy of 69%, suggesting that the high degree of
polarization recorded in the literature has started to manifest itself in
social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social
Informatic
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Hierarchical Character-Word Models for Language Identification
Social media messages' brevity and unconventional spelling pose a challenge
to language identification. We introduce a hierarchical model that learns
character and contextualized word-level representations for language
identification. Our method performs well against strong base- lines, and can
also reveal code-switching
Predicting Tweet Engagement with Graph Neural Networks
Social Networks represent one of the most important online sources to share
content across a world-scale audience. In this context, predicting whether a
post will have any impact in terms of engagement is of crucial importance to
drive the profitable exploitation of these media. In the literature, several
studies address this issue by leveraging direct features of the posts,
typically related to the textual content and the user publishing it. In this
paper, we argue that the rise of engagement is also related to another key
component, which is the semantic connection among posts published by users in
social media. Hence, we propose TweetGage, a Graph Neural Network solution to
predict the user engagement based on a novel graph-based model that represents
the relationships among posts. To validate our proposal, we focus on the
Twitter platform and perform a thorough experimental campaign providing
evidence of its quality.Comment: Accepted in ACM ICMR202
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