31,781 research outputs found
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM),
2017 IEEE/ACM International Conferenc
Event detection in location-based social networks
With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft
Entities as topic labels: Improving topic interpretability and evaluability combining Entity Linking and Labeled LDA
In order to create a corpus exploration method providing topics that are
easier to interpret than standard LDA topic models, here we propose combining
two techniques called Entity linking and Labeled LDA. Our method identifies in
an ontology a series of descriptive labels for each document in a corpus. Then
it generates a specific topic for each label. Having a direct relation between
topics and labels makes interpretation easier; using an ontology as background
knowledge limits label ambiguity. As our topics are described with a limited
number of clear-cut labels, they promote interpretability, and this may help
quantitative evaluation. We illustrate the potential of the approach by
applying it in order to define the most relevant topics addressed by each party
in the European Parliament's fifth mandate (1999-2004).Comment: in Proceedings of Digital Humanities 2016, Krako
Inferring Concept Prerequisite Relations from Online Educational Resources
The Internet has rich and rapidly increasing sources of high quality
educational content. Inferring prerequisite relations between educational
concepts is required for modern large-scale online educational technology
applications such as personalized recommendations and automatic curriculum
creation. We present PREREQ, a new supervised learning method for inferring
concept prerequisite relations. PREREQ is designed using latent representations
of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a
neural network based on the Siamese network architecture. PREREQ can learn
unknown concept prerequisites from course prerequisites and labeled concept
prerequisite data. It outperforms state-of-the-art approaches on benchmark
datasets and can effectively learn from very less training data. PREREQ can
also use unlabeled video playlists, a steadily growing source of training data,
to learn concept prerequisites, thus obviating the need for manual annotation
of course prerequisites.Comment: Accepted at the AAAI Conference on Innovative Applications of
Artificial Intelligence (IAAI-19
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