How does the mood management theory by Zillmann explain the impact of selective exposure in the consumption of news media and the resulting emotional responses of individuals in the context of political bias and manipulation?
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The mood management theory was developed by Zillmann, trying to explain the consumption of media messages and information in the context of the person’s mood at the time of consumption. The topic under consideration in the research is the bias in U.S. news content that has resulted from the increase in the number of alternative news and information technologies and sources in the country. News consumption is a highly emotional issue, especially on matters of politics. There are times when citizens lose it because of political mood swings based on what they consume from the media. Other times, what people are made to take up from different sources optimizes their feelings towards different situations. The mood management theory is an excellent concept of theorizing people’s emotions in different situations.
News media have been hijacked by political brokers and different divides to either sell their agenda or propaganda. People no longer consume factual news as they should; instead, they uptake what is convoluted to suit the storyline and desires of masters. Shao (2009) argued that the media has mastered the art of selective exposure. They feed people selectively, based on the outcome they desire. For instance, pro-Democrats media will report in a way that slams the Republicans and vice versa. This will help whip and optimize the negative emotions against the opponent. Different media platforms will focus on creating a particular image that will suit the end result.
Kursuncu (2018) looks into how much social media has been used in the recent past to create the mood around elections. In the article, the author looks into how former President Donald Trump used his social media platforms in 2016 to whip emotions and win the presidential election. The deliverers of news on social media focus on sentimentalism, sensationalism, and domain knowledge to build perspective (Kursuncu, 2018). The general public then uses the created perspective to make judgments and decisions.
Some situations are built on anxiety, others on happiness or sadness. Depending on how a piece of news is presented, people are likely to take a different interpretation (Havrylets et al., 2018). Nonetheless, views still insist on watching or reading what they well understand will have a negative impact on them. Broadcasting negative news is one way of winning people’s sympathy and pity. Giving positive news is likely to create a situation of likeableness and attraction. Therefore, news givers will package information in a way that either challenges the status quo or conforms to it, depending on what they want to achieve (Havrylets et al., 2018).
Each of the selected journal articles provides a crucial insight into the application of the mood management theory in media. The articles make reference to mood management in different ways. They especially converge at the point that news givers are looking for gratification through selective exposure. The main merit of all the sources is their inclination to the topic of choice for the research. They have specific references to media in their exploration of mood management theory. However, they have drawbacks too. The weakest of the three articles is Shao (2009) because its perspective came at a point where social media had not taken shape in the media industry. The most reliable source among the three is Kursuncu (2018), based on its modern take on the issue. Havrylets et al. (2018) give an amazing perspective, digging into the theory through the lenses of emotions, news, and mood caused by negative TV news. The article will be a central pillar of reference in justifying the thesis statement.
References (APA 6th Ed.)
Havrylets, Y., Tukaiev, S., Rizun, V., &Khylko, M. (2018). State anxiety, mood, and emotional effects of negative TV news depend on burnout. https://doi.org/10.31234/osf.io/m3xv2
Kursuncu, U., Gaur, M., Lokala, U., Thirunarayan, K., Sheth, A., &Arpinar, I. (2018). Predictive analysis on Twitter: Techniques and applications. Lecture Notes in Social Networks, 67-104. https://doi.org/10.1007/978-3-319-94105-9_4
Shao, G. (2009). Understanding the appeal of user‐generated media: A uses and gratification perspective. Internet Research, 19(1), 7-25. https://doi.org/10.1108/10662240910927795
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