33,750 research outputs found
Topological Data Mapping of Online Hate Speech, Misinformation, and General Mental Health: A Large Language Model Based Study
The advent of social media has led to an increased concern over its potential
to propagate hate speech and misinformation, which, in addition to contributing
to prejudice and discrimination, has been suspected of playing a role in
increasing social violence and crimes in the United States. While literature
has shown the existence of an association between posting hate speech and
misinformation online and certain personality traits of posters, the general
relationship and relevance of online hate speech/misinformation in the context
of overall psychological wellbeing of posters remain elusive. One difficulty
lies in the lack of adequate data analytics tools capable of adequately
analyzing the massive amount of social media posts to uncover the underlying
hidden links. Recent progresses in machine learning and large language models
such as ChatGPT have made such an analysis possible. In this study, we
collected thousands of posts from carefully selected communities on the social
media site Reddit. We then utilized OpenAI's GPT3 to derive embeddings of these
posts, which are high-dimensional real-numbered vectors that presumably
represent the hidden semantics of posts. We then performed various
machine-learning classifications based on these embeddings in order to
understand the role of hate speech/misinformation in various communities.
Finally, a topological data analysis (TDA) was applied to the embeddings to
obtain a visual map connecting online hate speech, misinformation, various
psychiatric disorders, and general mental health.Comment: 43 page
Deep Learning in the Automotive Industry: Applications and Tools
Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.Comment: 10 page
Dialogue as Data in Learning Analytics for Productive Educational Dialogue
This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers
Easy over Hard: A Case Study on Deep Learning
While deep learning is an exciting new technique, the benefits of this method
need to be assessed with respect to its computational cost. This is
particularly important for deep learning since these learners need hours (to
weeks) to train the model. Such long training time limits the ability of (a)~a
researcher to test the stability of their conclusion via repeated runs with
different random seeds; and (b)~other researchers to repeat, improve, or even
refute that original work.
For example, recently, deep learning was used to find which questions in the
Stack Overflow programmer discussion forum can be linked together. That deep
learning system took 14 hours to execute. We show here that applying a very
simple optimizer called DE to fine tune SVM, it can achieve similar (and
sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84
times faster hours than deep learning method.
We offer these results as a cautionary tale to the software analytics
community and suggest that not every new innovation should be applied without
critical analysis. If researchers deploy some new and expensive process, that
work should be baselined against some simpler and faster alternatives.Comment: 12 pages, 6 figures, accepted at FSE201
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced
after the administration of a certain drug or a combination of drugs,
presenting a challenge for drug development and drug administration. In this
paper, we present a set of taggers for extracting adverse drug reactions and
related entities, including factors, severity, negations, drug class and
animal. The systems used a mix of rule-based, machine learning (CRF) and deep
learning (BLSTM with word2vec embeddings) methodologies in order to annotate
the data. The systems were submitted to adverse drug reaction shared task,
organised during Text Analytics Conference in 2017 by National Institute for
Standards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.Comment: Paper describing submission for TAC ADR shared tas
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