Using Natural Language
Processing to Analyze News Bias
Bradley Erickson
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As we live in the information age, finding
reliable information becomes more and
more difficult. All news sources contain
bias, thus anything someone reads could
be misinformation or interpreted
differently. This research scrapes
thousands of articles from many sources
relating to 5 controversial topics: the
impeachment, abortion, immigration,
gun control, and climate change. We use
unsupervised learning techniques to
rank and filter out the top sentences by
giving more weight to unbiased, more
credible sources. This information will
form an extractive-based summary.
Finally, we compare the language used
among 3 bias groups: left bias, no bias,
and right bias. The research provides
insights into the current bias state of
news reporting.
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Social Influence and the Freshman Experience
Adam Funk
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We investigated the relationship
between time at a university and who
freshmen are most influenced by at that
given time. The study took a subset of
1000 freshmen from the Winona State
University class of 2023 and asked about
different influence factors and changes
in behavior over the course of 10 weeks.
Different surveys were sent out at 1
week, 3 weeks, 6 weeks, and 10 weeks.
Questions on who they were influenced
by, both negatively and positively, were
asked, as well as groups they were a part
of and whether they felt they had
changed as a person. We also attempted
to monitor the change over time of
specific individuals through an
identification code, which was
unsuccessful. However, summary
statistics of each time point were
successfully collected and reported.
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