Module 4

 Victor Chavez

Professor Andrews

Research Methods

9/25/2020

1 st draft of Literature review

People 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 people look up news to see what is going on with the world around them, there is a bias in the algorithm itself, when they look for news that people are not even aware, 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, with the main 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 its growing digital audience. Then we will examine the effects it has on the people consuming news through social media, and see if a bias emerges more from social media or media. In this review, the main 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 take a look at an algorithm that gives an individual data item a bias score associated with it (Kulshresta, 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’s 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. 

Search bias quantification and Antecedents of Retweeting in a(Political) Marketing Context

Now that we have an understanding of 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 lead 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.

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 or 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 on articles with little to no proof to back up these claims of there's, 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 are 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 have to choose a side, and anyone that disagrees with them is wrong on their political stance.

References

Walker, Lorna, et al. “Antecedents of Retweeting in a (Political) Marketing Context.” Psychology & Marketing, vol. 34, no. 3, 2017, pp. 275–293., doi:10.1002/mar.20988.

Waters, Richard D, et al. “Media Catching and the Journalist-Public Relations Practitioner Relationship: How Social Media Are Changing the Practice of Media Relations.” Journal of Public Relations Research, vol. 22, no. 3, 2010, pp. 241–264., doi:10.1080/10627261003799202.

Ardèvol-Abreu Alberto, and De Zúñiga Homero Gil. “Effects of Editorial Media Bias Perception and Media Trust on the Use of Traditional, Citizen, and Social Media News.” Journalism & Mass Communication Quarterly, vol. 94, no. 3, 2017.

Kulshrestha, Juhi, et al. “Search Bias Quantification: Investigating Political Bias in Social Media and Web Search.” Information Retrieval Journal, vol. 22, no. 1-2, 2019, pp. 188–227., doi:10.1007/s10791-018-9341-2.

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Eagly, H., & Chaiken, S. (1993). The psychology of attitudes, Fort Worth, TX: Harcourt Brace Jovanovich.

Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19, 39–52.

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