Phil 6.13.19

7:00 – 5:30 ASRC GEOS

PapersAndDatasets

  • Style Transfer in Text: Exploration and Evaluation
    • The ability to transfer styles of texts or images, is an important measurement of the advancement of artificial intelligence (AI). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose two models to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of lacking principle evaluation metrics, we propose two novel evaluation metrics that measure two aspects of style transfer: transfer strength and content preservation. We benchmark our models and the evaluation metrics on two style transfer tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with similar content preservation score but higher style transfer strength comparing to autoencoder.
  • Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media
    • In this paper, we analyze content sharing between news sources in the alternative and mainstream media using a dataset of 713K articles and 194 sources. We find that content sharing happens in tightly formed communities, and these communities represent relatively homogeneous portions of the media landscape. Through a mix-method analysis, we find several primary content sharing behaviors. First, we find that the vast majority of shared articles are only shared with similar news sources (i.e. same community). Second, we find that despite these echo-chambers of sharing, specific sources, such as The Drudge Report, mix content from both mainstream and conspiracy communities. Third, we show that while these differing communities do not always share news articles, they do report on the same events, but often with competing and counter-narratives. Overall, we find that the news is homogeneous within communities and diverse in between, creating different spirals of sameness.
  • Fear of missing out, or FOMO, is “a pervasive apprehension that others might be having rewarding experiences from which one is absent”.[2] This social anxiety[3] is characterized by “a desire to stay continually connected with what others are doing”.[2] FOMO is also defined as a fear of regret,[4] which may lead to a compulsive concern that one might miss an opportunity for social interaction, a novel experience, a profitable investment, or other satisfying events.[5] In other words, FOMO perpetuates the fear of having made the wrong decision on how to spend time since “you can imagine how things could be different”.
  • In financial panics, when social facts dominate over objective ones, the behavior is still called herding.
  • More JASS paper
    • Finished implementing Wayne’s suggestions
    • Save out DB – done
  • More clustering. Add options for column headers and row indices – done
  • Calculating the DTW for the initial data – stillllllll running.

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