The Spread of True and False News Online

Contributions

• Large-scale empirical evidence of differential diffusion: falsehood reached 1,500 people ~6× faster than truth; reached depth 19 nearly 10× faster than truth reached depth 10
• Comprehensive cascade metrics quantifying depth, size, breadth, and structural virality across verified true/false news
• Fact-checking dataset using 95–98% agreement from six independent fact-checking organizations (Snopes, PolitiFact, FactCheck, TruthOrFiction, HoaxSlayer, UrbanLegends)
• Human-vs.-bot analysis showing robots accelerate both truth and falsehood equally; humans, not automated systems, are responsible for the dramatic spread of false news
• Novelty hypothesis demonstrating false news is perceived as more novel, and that novel information elicits higher retweet likelihood, explaining the disparity

Method

Data collection: Rumor cascades on Twitter from 2006–2017. A cascade is defined as an unbroken retweet chain originating from a single tweet. Approximately 126,000 cascades spread by ~3 million users over 4.5 million times.

Verification: All cascades were fact-checked against verdicts from six independent fact-checking organizations, achieving 95–98% inter-organization agreement on true/false/mixed classifications.

Cascade quantification: Four metrics characterize each cascade:

1. Depth: Number of retweet hops from origin over time
2. Size: Number of users involved in cascade over time
3. Maximum breadth: Total unique users at any depth
4. Structural virality: Measure interpolating between single large broadcast vs. multi-generational spread with distributed contribution

Bot detection and removal: Used a sophisticated bot-detection algorithm to identify and exclude automated accounts, then recomputed findings with bots included to assess their differential contribution.

Results

Falsehood spreads more pervasively:

• Truth took ~6× longer to reach 1,500 people vs. falsehood
• Truth never exceeded cascade depth 10; falsehood reached depth 19 nearly 10× faster
• Falsehood diffused to more unique users at every cascade depth

Category-specific findings:

• False political news showed the most dramatic spread (depth, speed, breadth)
• Political, urban legend, and science news reached the most people overall
• Political and urban legend news spread fastest and were most viral

Retweeting likelihood:

• Falsehoods were 70% more likely to be retweeted than truth (controlling for many factors)

Human vs. bot responsibility:

• Bots accelerated both true and false news at similar rates
• Humans, not robots, are predominantly responsible for the differential spread of falsehood

Novelty as explanatory mechanism:

• False news was perceived as significantly more novel than true news
• Twitter users more likely to retweet novel information
• This aligns with information theory (novelty provides greatest decision-making aid) and social psychology (novel information signals insider status)

Notes

Strengths:

• Largest longitudinal study of misinformation diffusion at the time; comprehensive fact-checking across 126,000 cascades via multiple independent organizations
• Novel contribution separating human from bot behavior, dispelling myths about automated disinformation spread
• Clear, actionable finding: novelty preference explains the differential spread, moving beyond network structure or individual characteristics
• Implications for policy and intervention (behavioral vs. bot-centric approaches)
• Well-written research brief accessible to both practitioners and academics

Weaknesses:

• Limited to Twitter; generalizability to other platforms (Facebook, Reddit) unclear
• Temporal scope (2006–2017) predates modern platforms and algorithmic amplification changes
• Fact-checking limited to established fact-checking organizations; doesn't address emerging or non-English rumors
• Novelty metric inferred from linguistic features; causal mechanism not definitively established
• Policy implications (e.g., labeling interventions) suggested but not empirically validated in the brief

Follow-up opportunities:

• Cross-platform diffusion studies (Facebook, TikTok, Instagram)
• Interventions testing behavioral nudges vs. labels vs. friction
• Understanding how algorithmic amplification (post-2017) changes human-bot dynamics
• Multilingual and cross-cultural spread patterns
• Temporal evolution of false news: does novelty decay, and when does fact-checking reach users?