How Negative Media Coverage Impacts Platform Governance: Evidence from Facebook, Twitter, and YouTube

Figures & data

Table 1. Codebook for article classification.

Category code	Description	Example headline
Policy	Reporting on a social media company’s policies and strategic decisions, including the launch of new or changes to existing rules and policies. This includes discussions of how platforms enforce their own policies, rules, and standards; of the consequences of platforms’ policies for user, society, and politics; as well as users and civil society’s opinions and reactions to platform policymaking.	“YouTube pulls clips it says incite violence”
Products & Features	Reporting on a social media platform’s new products or features. This includes discussions of the impact of specific features or products for users and users’ opinions about platform’s features and products.	“New Twitter features: native video and ‘highlighted’ tweets”
Society	Reporting or opinion pieces about the place and consequences of a social media platform in general for society. This excludes articles about the impact of specific features or policies on users.	“Everyone’s over Instagram: How the app lost its purpose”
Employee	Reporting on the behavior and actions of a social media company’s employees. This includes articles on employee-led strike actions and whistleblowing.	‘Twitter employeefired for breaching security protocols’
Government & Regulation	Reporting on actions taken by government actors against platforms. This includes articles about government investigations into platforms’ actions and government restrictions on platform use.	“White House scrutinizes YouTube’s privacy rules”
Legal	Reporting on legal action from and taken against social media platforms. This includes discussions of lawsuits against platforms by individual citizens; lawsuits by platforms against individuals or organizations; and court rulings about the legal ramifications of social media use.	‘Twitter joke cannot get you fired, rules court’
Business	Business reporting. This includes articles that discuss a social media company’s business and growth (including earnings, sales, stock market position); and any acquisition of competitors.	“Facebook shares fell by 5% last quarter”
Miscellaneous	Reporting on platforms other than Facebook, YouTube, and Twitter; articles discussing something that happened on one of these platforms but not about the platforms themselves, and articles providing tips on how to use social media.	‘The QueenofEngland has joined Twitter’

Figure 1. Prevalence of different categories of media coverage.

Stacked colored bar chart, depicting the number of news articles published in one of the six topic categories (Business, Employee, Features, Legal, Policy, Politics & Regulation, Society) over a 15-year period.

Note: Number of articles per topic category per month.

Figure 2. Proportion of negative media coverage received by Facebook, Twitter, and YouTube. Percentage of negative articles published about each platform. Thirty weeks rolling average. The rugs indicate recorded policy changes.

Table 2. Proportion of negative and non-negative coverage.

	All		Facebook		Twitter		YouTube
	N	%	N	%	N	%	N	%
Negative	6,591	33.70%	4,921	37.43%	1,151	26.05%	519	25.99%
Non-Negative	12,969	66.30%	8,223	62.57%	3,268	73.95%	1,478	74.01%
Total	19,560	100%	13,144	100%	4,419	100%	1,997	100%

Table 3. Effect of cumulative negative coverage on future policy change.

	Dependent variable
	Policy Change
	(1)	(2)	(3)
Neg Coverage	1.06***	1.06***
	(1.02, 1.09)	(1.04, 1.09)
Neg Coverage – Policy			1.05*
			(1.00, 1.11)
Non-Neg Coverage	0.97***	0.97***	0.98***
	(0.96, 0.99)	(0.95, 0.99)	(0.96, 0.99)
Total Coverage	0.98***	0.98***	0.98***
	(0.97, 0.99)	(0.97, 0.99)	(0.97, 0.99)
Regulatory Pressure	0.87***	0.88**	0.95
	(0.82, 0.93)	(0.78, 0.99)	(0.87, 1.04)
Competitor Change	0.68***	0.68***	0.64**
	(0.53, 0.88)	(0.54, 0.85)	(0.46, 0.92)
Neg Coverage*Net Income		1.00
		(1.00, 1.00)
Net Income	1.00	1.00	1.00
	(1.00, 1.00)	(1.00, 1.00)	(1.00, 1.00)
Observations	1,395	1,395	1,395
R^2	0.06	0.06	0.06
Max. Possible R^2	0.14	0.14	0.14
Log Likelihood	−64.38	−64.32	−65.75
Wald Test	18.56*** (df = 6)	50.80*** (df = 7)	15.42** (df = 6)
LR Test	83.39*** (df = 6)	83.51*** (df = 7)	80.66*** (df = 6)
Score (Logrank) Test	54.68*** (df = 6)	56.10*** (df = 7)	53.60*** (df = 6)

Hazard ratios and 95%CI; *p, **p, ***p <.01.