Predicting AI News Credibility: Communicative or Social Capital or Both?

From Digital Culture & Society

Contents 1 Article Reference (APA) 1.1 Article Link & D.O.I. 2 Context 3 Overview 4 Strengths and Weaknesses 5 Assessment

[edit] Article Reference (APA)

Lee, S., Nah, S., Chung, D. S., & Kim, J. (2020). Predicting AI News Credibility: Communicative or Social Capital or Both? Communication Studies, 71(3), 428–447.

[edit] Article Link & D.O.I.

DOI: https://doi.org/10.1080/10510974.2020.1779769

Permalink: https://ocul-bu.primo.exlibrisgroup.com/permalink/01OCUL_BU/p5aakr/cdi_informaworld_taylorfrancis_310_1080_10510974_2020_1779769

[edit] Context

Gauging the trustworthiness of news has been a contentious issue for decades. Before the advent of AI technology in news applications, the credibility of news was still a prominent problem. There are the issues of ‘fake news’ that gained notoriety during the 2010s which caused a media storm of waves of news articles spreading disinformation along with mass accusations of articles being fake which caused a feeling of overall uncertainty of trust in the news industry. If media consumers are unsure if they can trust the news that is being reported, it can lead to unrest as the public may believe that the news organizations may be trying purposefully mislead them. With media distrust being at an all-time high, it will be important to see if the incorporation of AI in news articles will make a positive or negative impact on perceived news credibility.

[edit] Overview

This article uses their collected data to try and predict the future credibility of news created with the help of artificial intelligence. The authors explore the effects of ‘’communicative capital’’ (a resource that is built through interactions on both sides of a relationship or partnership) as well as ‘’social capital’’ (the relationship between a network and the local population) on AI news credibility. They do this by using data they collected to create various measurable statistics and charts to convey their found relationships. They collected this data through online surveys and panels to collect demographic information and news consumption habits. Overall their study found that the more audiences consume online news content, the more that they perceive AI news to be credible, trustworthy, and unbiased. These findings were consistent with previous studies that found that the level of consumption of news content was positively correlated to their feelings of trust in news media. Another one of their findings was that when comparing AI versus human-generated articles, the public and professional journalists rated the AI news content higher.

[edit] Strengths and Weaknesses

The article was strong in the way it backed its findings with data that was directly collected by the authors and not found from outside sources. The authors also showed the process of how they came to their conclusion by explaining the equations they used along with providing figures that showcased their work. They also used control variables to make the findings more credible to alleviate some doubt about data manipulation or a poor data set. They also branched out and explored three separate hypotheses so they could get more use out of their collected data and they were able to support all three of their predictions. This article may have done better by having a more narrow research objective as their claims and hypotheses seem to stretch too far from the main specific topic. This article also became very complex when they shared their results as the calculations and variables were not explained very well. The authors could have also benefited from a larger sample size from many demographics and countries that may have different feelings about their local news content.

[edit] Assessment

This article does a good job of asking questions and finding answers based on the data that was collected. Unlike other studies, this article used data that was directly collected which gives them a level of legitimacy over other works that may have used illegitimate data sets. The article also does a good job of exploring the complicated relationships between news entities and the public how they can change based on interactions and what consequences can be expected. This article is a little data-heavy and analytical which may make it hard to follow for some, but the information is valuable to the topic.

Rm18xz 23:29, 8 December 2023 (EST)