Editing Media bias (section)

=== Algorithms ===
{{See also|Algorithmic bias}}
[[Pol.is|Polis]] (or Pol.is) is a social media website that allows people to share their opinions and ideas while elevating ideas that have more consensus.<ref name=":02">{{Cite news |last=Miller |first=Carl |date=2020-09-27 |title=How Taiwan's 'civic hackers' helped find a new way to run the country |url=https://www.theguardian.com/world/2020/sep/27/taiwan-civic-hackers-polis-consensus-social-media-platform |access-date=2024-02-27 |work=The Guardian |language=en-GB |issn=0261-3077}}</ref> By September 2020, it had helped to form the core of dozens of pieces of legislation passed in Taiwan.<ref name=":02" /> Proponents had sought out a way to inform the government with the opinions of citizens between elections while also providing an online outlet for citizens that was less divisive and more informative than social media and other large websites.<ref name=":02" /><ref>{{Cite magazine |last=Miller |first=Carl |date=November 26, 2019 |title=Taiwan is making democracy work again. It's time we paid attention |url=https://www.wired.co.uk/article/taiwan-democracy-social-media |access-date=2024-02-27 |magazine=Wired UK |language=en-GB |issn=1357-0978}}</ref>

Attempts have also been made to utilize [[Machine learning|machine-learning]] to analyze the bias of text.<ref>{{cite conference |last1=Färber |first1=Michael |last2=Burkard |first2=Victoria |last3=Jatowt |first3=Adam |last4=Lim |first4=Sora |date=10 October 2020 |title=A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias |conference=The 29th ACM International Conference on Information & Knowledge Management |location=Virtual Event, Ireland |pages=3007–3014 |doi=10.1145/3340531.3412876 |doi-access=free}}</ref> For example, person-oriented framing analysis attempts to identify frames, i.e., "perspectives", in news coverage on a topic by determining how each person mentioned in the topic's coverage is portrayed.<ref name="Hamborg2021a">{{cite conference |last1=Hamborg |first1=Felix |last2=Heinser |first2=Kim |last3=Zhukova |first3=Anastasia |last4=Donnay |first4=Karsten |last5=Gipp |first5=Bela |date=2021 |title=2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) |publisher=IEEE |page= |pages=130–139 |doi=10.1109/JCDL52503.2021.00025 |isbn=978-1-6654-1770-9 |chapter=Newsalyze: Effective Communication of Person-Targeting Biases in News Articles |chapter-url=https://www.gipp.com/wp-content/papercite-data/pdf/hamborg2021a.pdf|arxiv=2110.09158 }}</ref><ref name="Hamborg">{{Cite journal |last1=Hamborg |first1=Felix |last2=Donnay |first2=Karsten |last3=Gipp |first3=Bela |year=2019 |title=Automated identification of media bias in news articles: An interdisciplinary literature review |url=https://www.gipp.com/wp-content/papercite-data/pdf/hamborg2018c.pdf |journal=International Journal on Digital Libraries |volume=20 |issue=4 |pages=391–415 |doi=10.1007/s00799-018-0261-y |doi-access=free}}</ref>

Another approach, matrix-based news aggregation can help to reveal differences in media coverage between different countries, for example.<ref name="Hamborg2018b">{{Cite journal |last1=Hamborg |first1=Felix |last2=Meuschke |first2=Norman |last3=Gipp |first3=Bela |year=2018 |title=Bias-aware news analysis using matrix-based news aggregation |url=http://www.gipp.com/wp-content/papercite-data/pdf/hamborg2018b.pdf |journal=[[International Journal on Digital Libraries]] |volume=21 |issue=2 |pages=129–147 |doi=10.1007/s00799-018-0239-9 |s2cid=49471192}}</ref>{{Primary source inline|date=March 2024}}