News sharing in social media is increasingly impacting the current distribution and consumption habits of news. Social media platforms such as Facebook and Twitter enable users to embed links to external online contents in their post and present these posts in a continuous feed. Facebook alone accounts for 43% of traffic to news sites and it has become the dominant way for people to obtain news on the Internet (Ingram, 2015, as cited in Keib et al., 2018).
Social media has dramatically changed the news ecosystem since the newspaper era and news publishers have lost controls over the distribution of news. In many cases, publications by news editors are replaced with information shared by friends, family and contact in form of social media posts, filtered by secret algorithms. These algorithms are designed to present all kinds of news media that are curated based on what companies think users would want to distribute and consume.
With the growing shift towards news distribution and consumption through social media and the increasingly use of algorithm, it is important for us as online news consumers to understand the influential factors behind online news sharing: how does social news sharing start to become a trend in media and communication? Who controls the key business in this field? Who benefits most from this trend? Are there any parties put in disadvantage instead? And, why?
Understanding the Genesis of Social News Sharing
News sharing is not native to digital platforms – such social practice predates the Internet and is an inherent communal activity (Carlson, 2016; Kalsnes & Larsson, 2018). Much like communication, digital technologies such as social media has accelerated the processes of news sharing. Before the emergence of social media, news sharing relied on the distribution of material artifacts such as sending newspaper clippings through postal services or passing on information through conversations.
It is important to note that the practice of news sharing differs from general information sharing (Ma, Lee, & Goh, 2013). News sharing is considered as a significant factor in societal evolution, agenda setting and the construction of public opinion. And social media shifts this concept of news sharing – a mediated activity carried out in public – from offline to online (Carlson, 2016).
News sharing has then changed from the sender-receiver dynamic of mass communication to a more complex network of collaborative community that enhances participation and interaction (Ma, Lee, & Goh, 2013). Sending newspaper clippings and conversing are no longer the only options to share news, community members can now join in the distribution process in form of liking, tagging, commenting and re-posting news posts (Dwyer & Martin, 2017).
However, this shift is also causing news organizations to have less control in online news distribution. An important question then emerges regarding social media news sharing: who holds the most power in online news distribution? Is it still the news organizations, as the producers of news? Or users, as the consumer of news? Or is it the social media platform itself as the medium of news?
The Dictator of Social News Sharing: Algorithm?
One particular characteristic of social media that makes news sharing distinct from traditional media of broadcast and print is the personalization of news based on users’ interest (Carlson, 2016). Platform such as Facebook is well-known in utilizing opaque algorithms to filter and organize their news feed, with the purpose of showing users contents that are most relevant to them (Tandoc & Maitra, 2018).
As Facebook assumes the role of news source, news organizations have increasingly publish their content on Facebook (Tandoc & Maitra, 2018; Ingram, 2015, as cited in Keib et al., 2018). The relationship between publishers and social media was supposedly straightforward and mutually beneficial: news organizations are able to distribute their content to the enormous amount of social media users and social media companies gain journalistic contents and traffic to the platform. However, this relationship is certainly more complicated than that, especially with the inclusion of the current algorithm of Facebook.
In traditional editorial selection process, publishers use news value as a criteria to determine the relevancy of a story that would influence audience attention, which is an important factor in news sharing (Devito, 2016). In Facebook news feed, news selection is not conducted by publishers, but by algorithm that uses algorithmic value as the decision-making criteria in relevancy.
To a certain extent, news organizations are able to control how their published contents are distributed in social media (Tandoc & Maitra, 2018). They are able to decide the story and its format and how the story is published as a social media post in accordance to the analytical data [e.g. interaction and audience reach] that Facebook provides for Page owners. However, Facebook also provides everyday users with a continuous and personalized news feed through algorithmic value by presenting them with content that suits their interests. With millions of users and hundreds of thousands of posts, social media platforms have to utilize algorithm to sort through importance, relevancy and recency to decide which posts should have been seen first. And as mentioned before, news sharing is a practice of distributing a specific content instead of general information (Ma, Lee, & Goh, 2013). The practice in online media includes providing audience with access to news content by posting, recommending or tagging other social media users (Kumpel, Karnowski, & Keyling, 2015). Hence according to the algorithmic value, users will be more likely to consume and further distribute contents that pique their interests.
The shift from news value to algorithmic value means that social media companies have enormous power in determining our news distribution and consumption habits. Of course social media platforms do not fully decide what we read – at least not in the traditional sense of decision making – but when one platform such as Facebook becomes the dominant source of news, its algorithm is undeniably influencing the accessibility and options of content and news organizations have to adapt their work to the demand of current trend in news sharing.
This then brings us to the next question to online news sharing: how does the current trend affect involving parties, such as news organizations, everyday users and social media companies in terms of political economic, societal and/or cultural?
The Impacts of Social News Sharing
Social Media Companies
As algorithm heavily influences our news distribution and consumption habits, social media companies have become overwhelmingly more powerful than news organizations in determining what we read. Hence, it is apparent that these companies, such as Facebook benefited the most from this current trend of social news sharing. By being the dominant controller and greatest concentration of power in the current media and communication field, social media companies have become enormously profitable from monetization of other people’s work and advertisements.
The current revenue model of online news sharing in monetization being advertisement driven has benefited not only the social media companies, but also everyday users (Chakraborty et al., 2017). Unlike paying subscription for newspapers and magazines, users do not have to pay anything to consume and re-distribute news since the money comes from clicks on advertisements in the social media posts or news sites through links. Algorithms also benefit users in sorting the ever increasing accessibility and availability of online content by prioritizing most popular or relevant posts for easier consumption (Tandoc & Maitra, 2018).
However, algorithms can also put everyday users in disadvantage. Algorithms are designed to give us what it thinks we want, in which the personalization of our news feed has been crafted to reinforce our pre-existing beliefs. Eli Pariser, the co-founder of Upworthy, coined the term “filter bubble” when talking about the personalization of our web: there will never be two webs that are the same, meaning we are unlikely to be exposed to information that broadens our beliefs or challenges our pre-existing views.
As mentioned earlier, the relationship between news organizations and social media was supposedly mutually beneficial, in which news organizations are able to distribute their content to millions of social media users (Tandoc & Maitra, 2018). However, algorithms can also put news organizations in difficult situation.
With news organizations relying on clicks as a source of revenue and the visibility of posts according to the algorithmic values, news organizations are in fierce competitions with one another to capture users’ attention (Chakraborty et al., 2017). A by-product of this competition is clickbaits, where news organizations take advantage of the curiosity gap in order to tempt audiences to click on their posts. Clickbaits are typically flashy headlines that pique the audience interests.
As discussed, algorithmic value is based on relevancy – and relevancy is influential to audience attention (Devito, 2016). George Loewenstein (1994), a professor at Carneige Mellon University, explored “The Psychology of Curiousity” and explained that curiosity arose when attention becomes more focused when there is a knowledge gap. Attracted by flashy headlines that promise more information in the actual news articles, users are drawn to click due to this curiosity gap.
As an example that can be seen in the infographic above, Upworthy has the most news shared article in Facebook compared to other news organizations. This is because Upworthy is utilizing ‘curiosity gap’ headlines that invite clicks through the right balance of information and flashy promises.
It is evident that social news sharing in this digital age is heavily influenced by algorithm. Living in the age of algorithm has changed the distribution and consumption of news and the business model of the media and communication field. It is important for us to know these processes to better understand news sharing habits.
Word Count: 1615
Bell, E. (2016, March 7). Facebook is eating the World. Columbia Journalism Review. Retrieved from https://www.cjr.org/analysis/facebook_and_media.php
Carlson, M. (2016). Embedded links, embedded meanings: Social media commentary and news sharing as mundane media criticism. Journalism Studies, 17(7), 915-924.
Chakraborty, A., Sarkar, R., Mrigen, A., & Ganguly, N. (2017). Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter. Social and Information Networks, 1(1), 1-20.
Devito, M. A. (2017) From Editors to Algorithms. Digital Journalism, 5(6), 753-773.
Jaffe, E. (2014, April 23). Upworthy’s Headlines Are Insufferable. Here’s Why You Click Anyway. Fast Company. Retrieved from https://www.fastcompany.com/3028193/upworthys-headlines-are-insufferable-heres-why-you-click-anyway
Kalnes, B. & Larsson, A. O. (2018). Understanding News Sharing Across Social Media: Detailing Distribution on Facebook and Twitter. Journalism Studies, 19(11), 1669-1688.
Keib, K. Espina, C., Lee, Y. I., Wojdynski, B. W., Choi, D., & Bang, H. (2018). Picture This: The Influence of Emotionally Valenced Images, On Attention, Selection, and Sharing of Social Media News. Media Psychology, 21(2), 202-221.
Kumpel, A. S., Karnowski, V., & Keyling, T. (2015). News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks. Social Media + Society, 1-4.
Loewenstein, G. (1994). The Psychology of Curiosity: A Review and Reinterpretation. Psychological Bulletin, 116(1), 75-98.
Ma, L., Lee, C. S., & Goh, D. H. L. (2013). Understanding News Sharing in Social Media. Emerald Insight, 38(5), 598-615.
Pariser, E. [TED Talk] (2011, March). Beware Online “Filter Bubbles” (video file). Retrieved from https://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles#t-85613
Reyes, A. D. (2013, December 26). The Curiosity Gap: Going Viral. The Guidon. Retrieved from http://www.theguidon.com/1112/main/2013/12/the-curiosity-gap-going-viral/
Tandoc, E. C. & Maitra, J. (2018). News organizations’ use of Native Videos on Facebook: Tweaking the journalistic field one algorithm change at a time. New Media & Society, 20(5), 1679-1696.
Viner, K. (2016, July 12). How Technology Disrupted the Truth. The Guardian. Retrieved from https://www.theguardian.com/media/2016/jul/12/how-technology-disrupted-the-truth