14 research outputs found
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed
can help people follow a healthy diet. We tackle the problem of food
ingredients recognition as a multi-label learning problem. We propose a method
for adapting a highly performing state of the art CNN in order to act as a
multi-label predictor for learning recipes in terms of their list of
ingredients. We prove that our model is able to, given a picture, predict its
list of ingredients, even if the recipe corresponding to the picture has never
been seen by the model. We make public two new datasets suitable for this
purpose. Furthermore, we prove that a model trained with a high variability of
recipes and ingredients is able to generalize better on new data, and visualize
how it specializes each of its neurons to different ingredients.Comment: 8 page
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
How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
Hypergraphs provide a natural way of representing group relations, whose
complexity motivates an extensive array of prior work to adopt some form of
abstraction and simplification of higher-order interactions. However, the
following question has yet to be addressed: How much abstraction of group
interactions is sufficient in solving a hypergraph task, and how different such
results become across datasets? This question, if properly answered, provides a
useful engineering guideline on how to trade off between complexity and
accuracy of solving a downstream task. To this end, we propose a method of
incrementally representing group interactions using a notion of n-projected
graph whose accumulation contains information on up to n-way interactions, and
quantify the accuracy of solving a task as n grows for various datasets. As a
downstream task, we consider hyperedge prediction, an extension of link
prediction, which is a canonical task for evaluating graph models. Through
experiments on 15 real-world datasets, we draw the following messages: (a)
Diminishing returns: small n is enough to achieve accuracy comparable with
near-perfect approximations, (b) Troubleshooter: as the task becomes more
challenging, larger n brings more benefit, and (c) Irreducibility: datasets
whose pairwise interactions do not tell much about higher-order interactions
lose much accuracy when reduced to pairwise abstractions
Twitter and Food Well-Being: Analysis of #SlowFood Postings Reflecting the Food Well-Being of Consumers
This study examines how hashtag #SlowFood postings on social media site Twitter reflect the food well-being of consumers. 4102 tweets containing the hashtag #SlowFood were identified. Using interpretive content analysis, only 210 food-content messages in English language, from consumers were selected coded and interpreted. Displays of positive emotions and activities related with the slow food consumption on social media were found. By studying how consumers share their food well-being on social media, this research contributes to the understanding of food well-being and how it is practiced online
Population-scale dietary interests during the COVID-19 pandemic.
The SARS-CoV-2 virus has altered people's lives around the world. Here we document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time series of Google search volumes. We find that during the first wave of the COVID-19 pandemic there was an overall surge in food interest, larger and longer-lasting than the surge during typical end-of-year holidays in Western countries. The shock of decreased mobility manifested as a drastic increase in interest in consuming food at home and a corresponding decrease in consuming food outside of home. The largest (up to threefold) increases occurred for calorie-dense carbohydrate-based foods such as pastries, bakery products, bread, and pies. The observed shifts in dietary interests have the potential to globally affect food consumption and health outcomes. These findings can inform governmental and organizational decisions regarding measures to mitigate the effects of the COVID-19 pandemic on diet and nutrition