1,763 research outputs found
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
With one billion monthly viewers, and millions of users discussing and
sharing opinions, comments below YouTube videos are rich sources of data for
opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset,
a freely-available collections of more than 50,000 YouTube comments and
metadata below autonomous vehicle (AV)-related videos. We describe its creation
process, its content and data format, and discuss its possible usages.
Especially, we do a case study of the first self-driving car fatality to
evaluate the dataset, and show how we can use this dataset to better understand
public attitudes toward self-driving cars and public reactions to the accident.
Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on
Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018
Social media mining for identification and exploration of health-related information from pregnant women
Widespread use of social media has led to the generation of substantial
amounts of information about individuals, including health-related information.
Social media provides the opportunity to study health-related information about
selected population groups who may be of interest for a particular study. In
this paper, we explore the possibility of utilizing social media to perform
targeted data collection and analysis from a particular population group --
pregnant women. We hypothesize that we can use social media to identify cohorts
of pregnant women and follow them over time to analyze crucial health-related
information. To identify potentially pregnant women, we employ simple
rule-based searches that attempt to detect pregnancy announcements with
moderate precision. To further filter out false positives and noise, we employ
a supervised classifier using a small number of hand-annotated data. We then
collect their posts over time to create longitudinal health timelines and
attempt to divide the timelines into different pregnancy trimesters. Finally,
we assess the usefulness of the timelines by performing a preliminary analysis
to estimate drug intake patterns of our cohort at different trimesters. Our
rule-based cohort identification technique collected 53,820 users over thirty
months from Twitter. Our pregnancy announcement classification technique
achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user
timelines. Analysis of the timelines revealed that pertinent health-related
information, such as drug-intake and adverse reactions can be mined from the
data. Our approach to using user timelines in this fashion has produced very
encouraging results and can be employed for other important tasks where
cohorts, for which health-related information may not be available from other
sources, are required to be followed over time to derive population-based
estimates.Comment: 9 page
Data analysis in social networks for agribusiness: a systematic review.
The ability of companies to react to changes imposed by the market can be aided by to information acquisition and knowledge generation. Big data technologies, crowdsourcing, and Online Social Networks (OSN) are used for knowledge generation. These technologies have assumed a significant position in agribusiness in recent decades. This work investigates how social network analysis can promote agribusiness to provide a basis for future applications and evaluations. We adopted a hybrid systematic mapping to conduct the investigation. Two hundred twenty-three works that propose solutions for agribusiness were found and categorized. Results showed the most used OSN is Twitter and revealed an increase in the number of studies in this area. The information obtained indicates how social media monitoring can complement traditional decision-making methods in managing and regulating agricultural systems. However, more studies in agribusiness using data analysis tools on social networks are required, considering the importance of social networks on marketing strategies. Based on our results, we discuss some challenges and research directions
What Facebook Messages Told Us About How We Handled Disaster Management during the COVID-19 Pandemic?
As COVID-19 continues, social media platforms such as Facebook have become an increasingly important tool for communication and information sharing for public and government agencies. The generic disaster management cycle (mitigation, preparedness, response, and recovery) provides systematic guidance to the public and government agencies to respond to the crisis and suggest appropriate measures for different disaster stages. In this study, we examine various trending topics and themes during the COVID-19 outbreak. Using this generic disaster management cycle as our guiding framework, we examine news topics\u27 evolution during the COVID-19 pandemic on Facebook during each of the four phases. Guided Latent Dirichlet Allocation (Guided LDA) is used for topic modeling to identify topics and themes, and text network analytics is used to understand the connectedness of these news topics during each phase and their evolution
Social Media Behaviour Analysis in Disaster-Response Messages of Floods and Heat Waves via Artificial Intelligence
This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform a qualitative analysis via topic inference in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with 7 additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection
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