Module 5
Final draft Lit Review
Users today consume
vast amounts of news in a short amount of time, using social media as their
preferred news outlet. As various authors who did research on the subject point
out “In case of polarizing topics like politics, where multiple competing
perspectives exist, the political bias in the top search results can play a
significant role in shaping public opinion towards (or away from) certain
perspectives” (Kulshresta, Eslami, Messias, Zafar, Gosh, Gummadi &
Karahalios, 2019). When users look up news to see what is going on with the
world around them, there is a bias in the algorithm itself, shaping their
opinion before they read the whole story; segregating people on their political
views, and making them unable to have a conversation with people that have a
different political opinions from them. The purpose of this review is to
provide a cohesive understanding of the bias in social media and search engines,
with the focus being on political news in the U.S., or what politicians say in
social media(twitter). The next thing that is going to be examined is how news
journalists started to transition to social media as their main source of
delivering news to their growing digital audience. Then we will examine the
effects it has on the people consuming the news through social media and see if
a bias emerges more from social media or media. In this review, the focus of it
will be on political bias (US) in social media(twitter) and analyzing why
certain politicians get retweeted more than others. We will also look at an
algorithm that gives an individual data item a bias score associated with it (Kulshrestha,
Eslami, Messias, Zafar, Gosh, Gummadi & Karahalios, 2019).
How Social Media are Changing the Practice of Media Relations
Before
we get into the political bias in social media it is important to understand
what caused the transition for journalists in the first place. Water’s defines the changing interplay
between journalists and public relations practitioners and to analyze the
phenomenon of “media catching” (Waters, Tindall & Morton, 2010). “Media
catching” is defined as thousands of practitioners being contacted by
journalists and others seeking specific materials for stories, blog postings,
and Web sites (Waters, Tindall & Morton, 2010). The value of this study is
the increase in the number of people that acknowledge traditional media losing
its dominance to social media (Waters, Tindall & Morton, 2010). For example, research has
examined the impact of news releases on how the media portray political
candidates (Kiousis, Mitrook, Wu, & Seltzer, 2006). By looking at these
studies we can see how political biases are easier to emerge using social
media, as journalists are trying to adapt to the needs of its consumers by
using social media; to find materials they could use to generate revenue for
the company that they are working for. The social media news release, launched
in February 2006, allows for readers and observers to interact, contribute, and
build on the content presented by organizations. Todd Drefen, principal at
SHIFT Communications, launched the social media news release because ‘‘the
banal, unhelpful, cookie-cutter press releases of yore have outlived their
pre-Internet usefulness’’ (Defren, 2006, p. 3). With the embedding of photos,
audio, and video and the linking to microblog and blog posts, the social media
news release is a vehicle to increase the
discovery rates of
media releases via search engines and to gain traction with bloggers and other
social media outlets who want quick, compressed details and information from
organizations. In response to journalists asking for sources for their stories,
Peter
Shankman created the
HARO group on a social networking site in November 2007. HARO allows everyday
people who are not professional communicators to join and become a source. HARO
connects journalists and sources without any intermediaries and without a fee.
The competition for a journalist’s attention remains; but instead of submitting
a story based on an organizational perspective, practitioners search for story
topics tossed out by journalists that are relevant to their organizations. To
draw a picture representing the current state of media catching, frequencies
were calculated for all the study’s content analysis variables in the 302
Twitter updates and 2,802 e-mail messages. As described earlier, the Twitter
updates
were sent out throughout the day when journalists had immediate, pressing deadlines. Instead, traditional mainstream media are some of the biggest players in media catching. The ones who play the biggest role in media catching is traditional media itself, as they are the ones that must find new sources of information daily to meet their consumers base needs. In order for traditional media to keep up with the constant churning of news on social media, HARO was created to meet journalists' need to find new sources of information in a faster time frame; connecting the source and the journalist directly with one another. At first glance it looks like this database is what journalists need to keep up with social media, the problem with this social networking site is that anyone can become a source of information. Since there are no intermediaries between the groups to fact check the source’s information. The quality of their information becomes less factual, as a result of this volume of resources that journalists encounter. These stories become sensationalized, because the sources of information look at the needs of the journalists themselves; changing parts of the story that source would provide them, to meet the criteria the journalist is looking for. There is another pressing factor that would make journalists desperate to take any new form of information that could be turned into a story, and that is pressing deadlines that they face. Forcing them to use HARO instead of taking the time to research their sources properly. To find ways to grab people's attention enough for them to click on it, or watching the report live on their T.V.[VC1] [VC2]
Search bias quantification and Antecedents of Retweeting in
a(Political) Marketing Context
Now that we understand
how social media became such an important tool for journalists to find the
information that they need to produce stories and report them to their digital
consumer base. We will be looking at the search bias that has emerged from the
political news going to the internet/social media. And finally, we use the
framework to compare the relative bias for two popular search systems—Twitter
social media search and Google web search—for queries related to politicians
and political events (Kulshrestha, Eslami, Messias, Zafar, Gosh, Gummadi &
Karahalios, 2019). Algorithmic systems have become ubiquitous in our modern
lives, and they exert great influence on many aspects of our daily lives,
including shaping news and information we are exposed to via information
retrieval algorithms (Kulshrestha,
Eslami, Messias, Zafar, Gosh, Gummadi & Karahalios, 2019). The potential
biases that search systems can introduce and users’ unquestionable trust in
search results have led to growing concerns about search systems’ impact on the
behavior of users, especially in scenarios where they may potentially misinform
or mislead the users (Kulshrestha, Eslami, Messias, Zafar, Gosh, Gummadi &
Karahalios, 2019). Algorithmic systems have become an essential part of our
lives, as it is convenient for us to use to find out what is going on in the
world of U.S. politics.
After quantifying the
bias in social media search, we proceed to use our quantification framework to
compare the relative bias for political searches on two popular search systems
- Twitter social media search and Google Web search. Our motivation for
performing this comparison is to make the biases of different channels more
visible and accessible to the users. Traditional media channels like Fox News
or CNN have often been scrutinized by academics (Ribeiro et al. 2015; Babaei et
al. 2018; Budak et al. 2016; Gentzkow and Shapiro 2010; Groseclose and Milyo
2005; Baron 2006; Munson et al. 2013b) as well as media watchdog groups (like
FAIR (fair.org) and AIM (aim.org)) for fairness, accuracy and balance in the
news they report. Additionally, tools have also been developed to mitigate or
expose the media bias (Purple Feed 2018; Park et al. 2009; Munson et al. 2013b;
https://twitter-app.mpi-sws.org/media-bias-monitor/;
https://mediabiasfactcheck.com) to users. However, the relative biases of newer
digital algorithmic channels like search systems are not as well studied and
documented yet, and thus users may not be taking their relative biases into
account while selecting the channel to get their information from. In fact,
many users believe that these algorithmically curated channels (as opposed to
human editorial curation) are powerful, infallible and thus unbiased (Eslami et
al. 2016; Springer et al. 2017), which is far from being true. This lack of
awareness can result in “blind faith” in search systems (Pan et al.2007) and
impairs the users from making an informed choice of which search channel to
use. It is well documented that traditional media channels, like Fox News or
CNN have been under scrutiny for some time now, and tools have been developed
to mitigate media bias in traditional media. The problem is that these
traditional media channels are adapting to this digital age of news, by doing
so they can take advantage of the algorithmic channels. These are not well
studied or documented. The users of this search system believe that these
stories posted by Fox or CNN are biased free, and completely factual. The users
believe that the algorithm itself is bias free, as it had no human involvement
when looking for political news. Most users do not realize the bias that they
tried to avoid in traditional media has adapted and found a new way of shaping
people's ideas and opinions before they even read the entire story. Re-creating
the bias, they desperately wanted to escape from.
Twitter has grown in
popularity over the last couple of years, as politicians are using this
platform to express their thoughts on political situations, speaking directly
to potential voters that may support them during election. Online political
campaigning has professionalized significantly, but particularly since the
advent of social media, with emphasis placed on party-based campaigns directed
from the center (Lee, 2014). It has the potential to enable individual
politicians to communicate directly with constituents and to disseminate their
messages as individuals as opposed to relying on party “campaign machines” to
communicate or to depending on journalists’ mediated messages(Walker, Baines,
Dimitriu & Macdonald, 2017). Source expertise refers to the perceived
competence of the source providing the information; experts are perceived to be
a source of valid assertions (Hovland & Weiss, 1951; Ohanian, 1990).
Further, source trustworthiness refers to the possible bias/incentives
reflected in the source’s information (Eagly & Chaiken, 1993). This allows
politicians to cut out the middleman, in this case it is journalists, allowing
them to speak directly to their voter base. By doing this they can immediately
comment or express their thoughts on anything that their voting base would be
interested in hearing, but by doing this they leave room for a bias to emerge.
As mentioned in the previous paragraph, there is an algorithm bias in the
search engine itself that shapes and warps public opinion before they even get
the full story of the events that occurred. Manipulating people that just wanted
to keep themselves informed on U.S. political news.
Effects of Media bias
As journalists rush to find new stories to get the
attention of the people to watch their show, visit their website, or follow
them on social media; they may focus on one point of the story to push out this
new information out faster for the public to consume. By applying this method
of journalism, people start to worry that there is a media bias towards
political news on both sides. Those who consume news from traditional media and
from social media will tend to get news from traditional news sources in the
future (Adevol-Abreu, Gil de Zuniga, 2017). Finally, consistent with H5,
negative perceptions about media bias (Wave 1) significantly reduce traditional
news media consumption (Wave 2) (β = −.060, p < .01). That is, the more the
people perceive news media as biased, the less they will consume mainstream
information in the future (Adevol-Abreu, Gil de Zuniga, 2017). An individual
might choose to get news from a source they do not trust, just to stay in touch
with the mainstream interpretation of reality, to have a topic of conversation
to talk with their coworkers, or simply to pass the time (Adevol-Abreu, Gil de
Zuniga, 2017). That is, those who trust information from alternative media
(i.e., blogs and citizen media) are using social media as an entry gate to
those alternative sources. The fact that most of the alternative information
and citizen-generated news are distributed through social media (Newman et al.,
2012) gives support to this interpretation (Adevol-Abreu, Gil de Zuniga, 2017).
Citizen media is defined as a private citizen that makes their own news content
but are not actual journalists. They write opinion-based articles with little
to no proof to back up these claims of theirs, and social media is a great way
of finding these alternative news sources.
Conclusion
More and more social
media (twitter) is starting to play an important role in our lives, especially
when it comes to politics. As journalists made the transition to use this
platform to acquire news information, from politicians directly; politicians
are using this platform to communicate with their audience directly. People
that want to be updated on political news that is going in the U.S ; they will
use social media (twitter) or a search engine (google) to find it, but there is
algorithm bias that points people to specific stories or part of a story that
starts to influence their opinion on the subject, forcing them to take a stand
right away. The more people use social media the more likely, they will find
alternative news sources that are created by private citizens that write opinion-based
articles that reinforce this bias in social media. Forcing people to believe
that they must choose a side, and anyone that disagrees with them is wrong on
their political stance, worse yet evil. It is important to understand that
algorithms have became an essential in our lives, and most users blindly
believe that they are biased free.
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