634 research outputs found
Temporal Cross-Media Retrieval with Soft-Smoothing
Multimedia information have strong temporal correlations that shape the way
modalities co-occur over time. In this paper we study the dynamic nature of
multimedia and social-media information, where the temporal dimension emerges
as a strong source of evidence for learning the temporal correlations across
visual and textual modalities. So far, cross-media retrieval models, explored
the correlations between different modalities (e.g. text and image) to learn a
common subspace, in which semantically similar instances lie in the same
neighbourhood. Building on such knowledge, we propose a novel temporal
cross-media neural architecture, that departs from standard cross-media
methods, by explicitly accounting for the temporal dimension through temporal
subspace learning. The model is softly-constrained with temporal and
inter-modality constraints that guide the new subspace learning task by
favouring temporal correlations between semantically similar and temporally
close instances. Experiments on three distinct datasets show that accounting
for time turns out to be important for cross-media retrieval. Namely, the
proposed method outperforms a set of baselines on the task of temporal
cross-media retrieval, demonstrating its effectiveness for performing temporal
subspace learning.Comment: To appear in ACM MM 201
Identifying Retweetable Tweets with a Personalized Global Classifier
In this paper we present a method to identify tweets that a user may find
interesting enough to retweet. The method is based on a global, but
personalized classifier, which is trained on data from several users,
represented in terms of user-specific features. Thus, the method is trained on
a sufficient volume of data, while also being able to make personalized
decisions, i.e., the same post received by two different users may lead to
different classification decisions. Experimenting with a collection of approx.\
130K tweets received by 122 journalists, we train a logistic regression
classifier, using a wide variety of features: the content of each tweet, its
novelty, its text similarity to tweets previously posted or retweeted by the
recipient or sender of the tweet, the network influence of the author and
sender, and their past interactions. Our system obtains F1 approx. 0.9 using
only 10 features and 5K training instances.Comment: This is a long paper version of the extended abstract titled "A
Personalized Global Filter To Predict Retweets", of the same authors, which
was published in the 25th ACM UMAP conference in Bratislava, Slovakia, in
July 201
Extracting News Events from Microblogs
Twitter stream has become a large source of information for many people, but
the magnitude of tweets and the noisy nature of its content have made
harvesting the knowledge from Twitter a challenging task for researchers for a
long time. Aiming at overcoming some of the main challenges of extracting the
hidden information from tweet streams, this work proposes a new approach for
real-time detection of news events from the Twitter stream. We divide our
approach into three steps. The first step is to use a neural network or deep
learning to detect news-relevant tweets from the stream. The second step is to
apply a novel streaming data clustering algorithm to the detected news tweets
to form news events. The third and final step is to rank the detected events
based on the size of the event clusters and growth speed of the tweet
frequencies. We evaluate the proposed system on a large, publicly available
corpus of annotated news events from Twitter. As part of the evaluation, we
compare our approach with a related state-of-the-art solution. Overall, our
experiments and user-based evaluation show that our approach on detecting
current (real) news events delivers a state-of-the-art performance
Node Embedding over Temporal Graphs
In this work, we present a method for node embedding in temporal graphs. We
propose an algorithm that learns the evolution of a temporal graph's nodes and
edges over time and incorporates this dynamics in a temporal node embedding
framework for different graph prediction tasks. We present a joint loss
function that creates a temporal embedding of a node by learning to combine its
historical temporal embeddings, such that it optimizes per given task (e.g.,
link prediction). The algorithm is initialized using static node embeddings,
which are then aligned over the representations of a node at different time
points, and eventually adapted for the given task in a joint optimization. We
evaluate the effectiveness of our approach over a variety of temporal graphs
for the two fundamental tasks of temporal link prediction and multi-label node
classification, comparing to competitive baselines and algorithmic
alternatives. Our algorithm shows performance improvements across many of the
datasets and baselines and is found particularly effective for graphs that are
less cohesive, with a lower clustering coefficient
LEARNING WORD RELATEDNESS OVER TIME FOR TEMPORAL RANKING
Queries and ranking with temporal aspects gain significant attention in field of Information Retrieval. While searching for articles published over time, the relevant documents usually occur in certain temporal patterns. Given a query that is implicitly time sensitive, we develop a temporal ranking using the important times of query by drawing from the distribution of query trend relatedness over time. We also combine the model with Dual Embedding Space Model (DESM) in the temporal model according to document timestamp. We apply our model using three temporal word embeddings algorithms to learn relatedness of words from news archive in Bahasa Indonesia: (1) QT-W2V-Rank using Word2Vec (2) QT-OW2V-Rank using OrthoTrans-Word2Vec (3) QT-DBE-Rank using Dynamic Bernoulli Embeddings. The highest score was achieved with static word embeddings learned separately over time, called QT-W2V-Rank, which is 66% in average precision and 68% in early precision. Furthermore, studies of different characteristics of temporal topics showed that QT-W2V-Rank is also more effective in capturing temporal patterns such as spikes, periodicity, and seasonality than the baselines
Creator Context for Tweet Recommendation
When discussing a tweet, people usually not only refer to the content it
delivers, but also to the person behind the tweet. In other words, grounding
the interpretation of the tweet in the context of its creator plays an
important role in deciphering the true intent and the importance of the tweet.
In this paper, we attempt to answer the question of how creator context
should be used to advance tweet understanding. Specifically, we investigate the
usefulness of different types of creator context, and examine different model
structures for incorporating creator context in tweet modeling. We evaluate our
tweet understanding models on a practical use case -- recommending relevant
tweets to news articles. This use case already exists in popular news apps, and
can also serve as a useful assistive tool for journalists. We discover that
creator context is essential for tweet understanding, and can improve
application metrics by a large margin. However, we also observe that not all
creator contexts are equal. Creator context can be time sensitive and noisy.
Careful creator context selection and deliberate model structure design play an
important role in creator context effectiveness
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