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Fact-checking is the process of verifying the factual accuracy of questioned reporting and statements. Fact-checking can be conducted before (ante hoc) or after (post hoc) the text or content is published or otherwise disseminated. Internal fact-checking is such checking done in-house by the publisher to prevent inaccurate content from being published; when the text is analyzed by a third party, the process is called external fact-checking. Research suggests that fact-checking can indeed correct perceptions among citizens, as well as discourage politicians from spreading false or misleading claims. However, corrections may decay over time or be overwhelmed by cues from elites who promote less accurate claims. Political fact-checking is sometimes criticized as being opinion journalism. A review of US politics fact-checkers shows a mixed result of whether fact-checking is an effective way to reduce misconceptions, and whether the method is reliable. Sensationalist newspapers in the 1850s and later led to a gradual need for a more factual media. Colin Dickey has described the subsequent evolution of fact-checking. Key elements were the establishment of Associated Press in the 1850s (short factual material needed), Ralph Pulitzer of the New York World (his Bureau of Accuracy and Fair Play, 1912), Henry Luce and Time magazine (original working title: Facts), and the famous fact-checking department of The New Yorker. More recently, the mainstream media has come under severe economic threat from online startups. In addition the rapid spread of misinformation and conspiracy theories via social media is slowly creeping into mainstream media. One solution is for more media staff to be assigned a fact-checking role, as for example The Washington Post. Independent fact-checking organisations have also become prominent, such as PolitiFact. Ante hoc fact-checking aims to identify errors so that the text can be corrected before dissemination, or perhaps rejected.
[2]Paolo Prandoni, [3]LCAV, EPFL
In [1]:
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as sp
import seaborn as sns
sns.set()
In [2]:
plt.rcParams["figure.figsize"] = (14,4)
The basic reproduction number[4]¶
The propagation mechanism of an epidemic such as the one caused by
SARS-CoV-2 virus can be roughly modeled, at least in its inital phase,
as a process in which each infected individ [2]Paolo Prandoni, [3]LCAV, EPFL
The Karplus-Strong algorithm is a simple method to generate synthetic
sounds with arbitrary pitch. In this notebook we will explore a few
variations of the method to illustrate some basic notions of
discrete-time signal processing.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
In [2]:
plt.rcParams["figure.figsize"] [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will investigate the effect of phase on the
perceptual quality of a sound. It is often said that the human ear is
commercial-grade audio equipment takes place in the magnitude domain
only.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
from scipy.io import wavfile
In [2]:
plt.rcParams["figure.fig [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will explore various ways to implement a system
that changes the voice of a speaker. Typical applications for these
techniques, strictly sorted by plausibility, include international spy
missions and prank telephone calls, and devices such as the one
displayed here can be easily bought on the web... or in a novelty shop.
No description has been provided for t