But this occasional timidity is characteristic of almost all herding creatures. Though banding together in tens of thousands, the lion-maned buffaloes of the West have fled before a solitary horseman. Witness, too, all human beings, how when herded together in the sheepfold of a theatre’s pit, they will, at the slightest alarm of fire, rush helter-skelter for the outlets, crowding, trampling, jamming, and remorselessly dashing each other to death. Best, therefore, withhold any amazement at the strangely gallied whales before us, for there is no folly of the beasts of the earth which is not infinitely outdone by the madness of men.
—-Moby Dick, The Grand Armada
8:30 – 4:30 ASRC MKT
- Finished BIC and put the notes on Phlog
- Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media
- There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for one month that exposed them to messages produced by elected officials, organizations, and other opinion leaders with opposing political ideologies. Respondents were re-surveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative post-treatment, and Democrats who followed a conservative Twitter bot became slightly more liberal post-treatment. These findings have important implications for the interdisciplinary literature on political polarization as well as the emerging field of computational social science.
- More Keras
- hyperopt is a Python library for optimizing over awkward search spaces with real-valued, discrete, and conditional dimensions.
- One Hidden Layer:
training label size = 60000 test label size = 10000 60000 train samples 10000 test samples _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 128) 100480 _________________________________________________________________ activation_1 (Activation) (None, 128) 0 _________________________________________________________________ dense_2 (Dense) (None, 128) 16512 _________________________________________________________________ activation_2 (Activation) (None, 128) 0 _________________________________________________________________ dense_3 (Dense) (None, 128) 16512 _________________________________________________________________ activation_3 (Activation) (None, 128) 0 _________________________________________________________________ dense_4 (Dense) (None, 10) 1290 _________________________________________________________________ activation_4 (Activation) (None, 10) 0 ================================================================= Total params: 134,794 Trainable params: 134,794 Non-trainable params: 0
- Two hidden layers:
training label size = 60000 test label size = 10000 60000 train samples 10000 test samples _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 128) 100480 _________________________________________________________________ activation_1 (Activation) (None, 128) 0 _________________________________________________________________ dense_2 (Dense) (None, 128) 16512 _________________________________________________________________ activation_2 (Activation) (None, 128) 0 _________________________________________________________________ dense_3 (Dense) (None, 128) 16512 _________________________________________________________________ activation_3 (Activation) (None, 128) 0 _________________________________________________________________ dense_4 (Dense) (None, 10) 1290 _________________________________________________________________ activation_4 (Activation) (None, 10) 0 ================================================================= Total params: 134,794 Trainable params: 134,794 Non-trainable params: 0
viztales 
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