12,921 research outputs found
Detecting and Monitoring Hate Speech in Twitter
Social Media are sensors in the real world that can be used to measure the pulse of societies.
However, the massive and unfiltered feed of messages posted in social media is a phenomenon that
nowadays raises social alarms, especially when these messages contain hate speech targeted to a
specific individual or group. In this context, governments and non-governmental organizations
(NGOs) are concerned about the possible negative impact that these messages can have on individuals
or on the society. In this paper, we present HaterNet, an intelligent system currently being used by
the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that
identifies and monitors the evolution of hate speech in Twitter. The contributions of this research
are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social
network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on
hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification
approaches based on different document representation strategies and text classification models. (4)
The best approach consists of a combination of a LTSM+MLP neural network that takes as input the
tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area
under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the
literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation
grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge
On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach
Social Bots for Online Public Health Interventions
According to the Center for Disease Control and Prevention, in the United
States hundreds of thousands initiate smoking each year, and millions live with
smoking-related dis- eases. Many tobacco users discuss their habits and
preferences on social media. This work conceptualizes a framework for targeted
health interventions to inform tobacco users about the consequences of tobacco
use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that
leverages machine learning to identify users posting pro-tobacco tweets and
select individualized interventions to address their interest in tobacco use.
We searched the Twitter feed for tobacco-related keywords and phrases, and
trained a convolutional neural network using over 4,000 tweets dichotomously
manually labeled as either pro- tobacco or not pro-tobacco. This model achieves
a 90% recall rate on the training set and 74% on test data. Users posting pro-
tobacco tweets are matched with former smokers with similar interests who
posted anti-tobacco tweets. Algorithmic matching, based on the power of peer
influence, allows for the systematic delivery of personalized interventions
based on real anti-tobacco tweets from former smokers. Experimental evaluation
suggests that our system would perform well if deployed. This research offers
opportunities for public health researchers to increase health awareness at
scale. Future work entails deploying the fully operational Notobot system in a
controlled experiment within a public health campaign
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media
Sentiment analysis has been emerging recently as one of the major natural
language processing (NLP) tasks in many applications. Especially, as social
media channels (e.g. social networks or forums) have become significant sources
for brands to observe user opinions about their products, this task is thus
increasingly crucial. However, when applied with real data obtained from social
media, we notice that there is a high volume of short and informal messages
posted by users on those channels. This kind of data makes the existing works
suffer from many difficulties to handle, especially ones using deep learning
approaches. In this paper, we propose an approach to handle this problem. This
work is extended from our previous work, in which we proposed to combine the
typical deep learning technique of Convolutional Neural Networks with domain
knowledge. The combination is used for acquiring additional training data
augmentation and a more reasonable loss function. In this work, we further
improve our architecture by various substantial enhancements, including
negation-based data augmentation, transfer learning for word embeddings, the
combination of word-level embeddings and character-level embeddings, and using
multitask learning technique for attaching domain knowledge rules in the
learning process. Those enhancements, specifically aiming to handle short and
informal messages, help us to enjoy significant improvement in performance once
experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in
IJCVR on September 201
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