Monitoring of leading Belarusian state YouTube channels, May 2025

Monitorings

In May 2025, 1,584 videos from five Belarusian state-owned YouTube channels were analysed.

Daily channel activity

The STVBY YouTube channel (red line) shows the highest activity with sharp peaks of up to 25 videos per day.
Peak activity: 9 May is associated with Victory Day.
Volatility: All channels show uneven activity with sharp declines and increases.
NEWS.BY (yellow) and ONT TV Channel (green) are the second most active
Coordination: Some peaks in activity coincide between channels, which may indicate coordinated information campaigns.

Frequency of disinformation narratives

Let’s highlight the top 5 disinformation narratives in May 2025:

  1. Manipulation – 1,372 cases;
  2. Polarisation – 495 cases;
  3. Emotional exploitation – 331 cases;
  4. Conspiracy – 119 cases;
  5. Discrediting – 58 cases;

Manipulation accounts for 60% of all identified narratives.
There is a huge gap between first and second place (1,372 vs 495).
Emotional impact accounts for a significant share.

Distribution of risk levels by channel

Analysis by channel:

  • NEWS.BY: Highest percentage of high-risk content (~15-20%)
  • CТВBY: Average risk level with a predominance of medium-risk content
  • ONT TV Channel, BelTA, CBTV: Similar patterns with a moderate prevalence of medium- and low-risk content

Methodology for calculating the Disinformation Risk Index (IDI)

Basic IDI formula
IDO = (CRC × 0.30 + IDI × 0.25 + CM × 0.20 + IVD × 0.25) × 100

Result: percentage value of the overall disinformation risk of the channel


Formula components


CRC – Channel risk coefficient (weight: 30%)
CRC = (HIGH_video × 3 + MEDIUM_video × 2 + LOW_video × 1) / Total_number_of_videos

Range: 0-3 (the higher, the more dangerous)

Description: Directly measures the level of risk of content based on expert assessment

IDI – Disinformation Intensity Index (weight: 25%)
DII = (Number_of_narratives / Total_number_of_videos) × 100

Range: 0-∞% (usually 0-200%)

Description: Shows the saturation of content with disinformation narratives

KM – Manipulation coefficient (weight: 20%)

MC = (Manipulation_narratives / Total_number_of_narratives) × 100

Range: 0-100%

Description: The share of manipulative techniques among all identified narratives

IDI – Disinformation Engagement Index (weight: 25%)

DIE = ((Likes + Comments) / Views) × CR × 100

Range: 0-∞% (usually 0-50%)

Description: The effectiveness of risky content on the audience

Calculation example for the channel ‘NEWS.BY’
CRC = 2.17
High risk
IDI = 150%
Intense narratives
CM = 53.3%
Many manipulations
IVD = 15.2%
Active audience
Step-by-step calculation:
Step 1: Normalisation of values
CRI = 2.17, IDI = 1.50, CM = 0.533, IVD = 0.152

Step 2: Application of weights
IDD = (2.17 × 0.30 + 1.50 × 0.25 + 0.533 × 0.20 + 0.152 × 0.25) × 100

Step 3: Calculation
IDO = (0.651 + 0.375 + 0.107 + 0.038) × 100

Result: IDO = 117.1


IDO interpretation scale

IDO range Danger level Description Status
0-25 Low Minimal disinformation activity SAFE
25-50 Moderate Occasional problems, requires monitoring CAUTION
50-75 High Systematic misinformation DANGEROUS
75-100 Critical Intense disinformation machine ALERT
100+ Extreme Professional disinformation centre CRITICAL


Justification of weight coefficients

Component Weight Justification
CRC 30% The most important indicator is a direct expert assessment of the content’s danger.
IDI 25% Intensity = number of attempts to influence the audience
IVD 25% Effectiveness of impact and actual audience reach
KM 20% Specialisation in specific manipulative techniques

Conclusions

87% of content contains manipulative narratives (1,372 out of ~1,584 videos). Systematic nature of disinformation across all state channels.
Influence tactics:

  • Manipulation as the main tool of influence
  • Polarisation of society through contrasting narratives
  • Emotional exploitation to bypass critical thinking
  • Conspiracy theories to create an alternative reality

Coordination patterns:

  • Synchronised peaks of activity between channels
  • Uniformity of narrative strategies
  • Targeted information campaigns on key dates

An interactive table of disinformation narratives can be found here.
Data analysis and visualisation were performed using the R programming language.

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