104,914 research outputs found
Language in Our Time: An Empirical Analysis of Hashtags
Hashtags in online social networks have gained tremendous popularity during
the past five years. The resulting large quantity of data has provided a new
lens into modern society. Previously, researchers mainly rely on data collected
from Twitter to study either a certain type of hashtags or a certain property
of hashtags. In this paper, we perform the first large-scale empirical analysis
of hashtags shared on Instagram, the major platform for hashtag-sharing. We
study hashtags from three different dimensions including the temporal-spatial
dimension, the semantic dimension, and the social dimension. Extensive
experiments performed on three large-scale datasets with more than 7 million
hashtags in total provide a series of interesting observations. First, we show
that the temporal patterns of hashtags can be categorized into four different
clusters, and people tend to share fewer hashtags at certain places and more
hashtags at others. Second, we observe that a non-negligible proportion of
hashtags exhibit large semantic displacement. We demonstrate hashtags that are
more uniformly shared among users, as quantified by the proposed hashtag
entropy, are less prone to semantic displacement. In the end, we propose a
bipartite graph embedding model to summarize users' hashtag profiles, and rely
on these profiles to perform friendship prediction. Evaluation results show
that our approach achieves an effective prediction with AUC (area under the ROC
curve) above 0.8 which demonstrates the strong social signals possessed in
hashtags.Comment: WWW 201
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
COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content
MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user
An MPEG-7 scheme for semantic content modelling and filtering of digital video
Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users
A semantic-based platform for the digital analysis of architectural heritage
This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings
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