3 research outputs found
Automatic Conditional Generation of Personalized Social Media Short Texts
Automatic text generation has received much attention owing to rapid
development of deep neural networks. In general, text generation systems based
on statistical language model will not consider anthropomorphic
characteristics, which results in machine-like generated texts. To fill the
gap, we propose a conditional language generation model with Big Five
Personality (BFP) feature vectors as input context, which writes human-like
short texts. The short text generator consists of a layer of long short memory
network (LSTM), where a BFP feature vector is concatenated as one part of input
for each cell. To enable supervised training generation model, a text
classification model based convolution neural network (CNN) has been used to
prepare BFP-tagged Chinese micro-blog corpora. Validated by a BFP linguistic
computational model, our generated Chinese short texts exhibit discriminative
personality styles, which are also syntactically correct and semantically
smooth with appropriate emoticons. With combination of natural language
generation with psychological linguistics, our proposed BFP-dependent text
generation model can be widely used for individualization in machine
translation, image caption, dialogue generation and so on.Comment: published in PRICAI 201
Emotional and mental nuances and technological approaches: Optimising Fact-Check dissemination through cognitive reinforcement technique â€
The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and (Formula presented.) (formerly Twitter).FCT -Fundação para a Ciência e a Tecnologia(2022.06822