USA & Other Threat Assessment using the DTM Model.

Here’s the terrorism risk assessment using the Price-Oquindo Model with specific real-world examples derived from current misinformation trends on social media, highlighting their potential to exacerbate conditions that could lead to terrorism.

Risk Assessment Model Application with Case Studies:

1. Socio-Economic Status (SES) – Weight (α):

• Case Study: In economically unstable countries like Yemen and Pakistan, social media misinformation has targeted grievances around poverty and corruption. Extremist groups, like Al-Qaeda, exploit these narratives to recruit individuals from economically disenfranchised backgrounds, as seen in the Al-Qaeda operations in Yemen .

• Likelihood Score: 70/100

• Rationale: Misinformation around socio-economic disparities in these regions increases the chances of radicalization, particularly when people see extremist groups offering social services where the government fails. Misinformation can amplify anger, pushing vulnerable groups toward extremist actions.

2. Political Instability (PI) – Weight (β):

• Case Study: In 2024 U.S. elections, platforms like X (formerly Twitter) have become breeding grounds for disinformation. False narratives about election fraud and political manipulation, such as claims spread during the 2020 U.S. elections, continue to circulate, undermining democratic institutions. Similar campaigns have targeted the Russian-Ukraine conflict, with both state and non-state actors using social media to fuel tensions  .

• Likelihood Score: 90/100

• Rationale: With elections approaching in multiple regions, the combination of widespread political disinformation and already fragile democracies increases the risk of instability turning into violence or terrorism.

3. Ideological Extremism (IE) – Weight (γ):

• Case Study: ISIS and Al-Qaeda continue to use social media to push extremist ideologies, often under the guise of religious teachings. Their propaganda has evolved to target specific groups, leveraging anger over foreign policies and social grievances. This is particularly notable in regions like Iraq and Syria, where social media amplifies extremist content  .

• Likelihood Score: 80/100

• Rationale: Ideological extremism, spread via platforms such as Facebook and Telegram, makes it easier for extremist groups to radicalize individuals globally. These narratives can rapidly escalate into terrorist actions.

4. Social Disenfranchisement (SD) – Weight (δ):

• Case Study: Far-right groups in Western Europe use social media to amplify narratives about immigration and national identity, leading to feelings of alienation among certain groups. For example, disenfranchised youths in France have turned to violent extremist groups after feeling excluded from mainstream society  .

• Likelihood Score: 70/100

• Rationale: Misinformation exacerbates social exclusion, particularly in marginalized communities, making them more susceptible to radicalization. Social media allows extremists to exploit these feelings of disenfranchisement.

5. Lack of Education (LE) – Weight (ϵ):

• Case Study: In regions like Afghanistan, where access to formal education is limited, misinformation about Western values or policies is frequently used by the Taliban to manipulate perceptions and foster distrust in foreign governments. This disinformation directly leads to radicalization, as seen in the recruitment tactics employed by the Taliban  .

• Likelihood Score: 60/100

• Rationale: In regions with low education levels, individuals lack the critical thinking skills needed to assess the credibility of information. This makes them more vulnerable to radicalization through social media.

6. Psychosocial Factors (PSF) – Weight (ζ):

• Case Study: Post-pandemic mental health issues are being exploited by extremist groups in the West to radicalize isolated individuals. In Europe, far-right extremists have targeted individuals affected by lockdowns, economic uncertainty, and social isolation, framing their narratives as solutions to personal and societal crises  .

• Likelihood Score: 80/100

• Rationale: Social media misinformation targeting mental health vulnerabilities is on the rise, increasing the likelihood of radicalization, especially among isolated and vulnerable individuals.

7. External Influence (EI) – Weight (η):

• Case Study: Russia and China are known for pushing misinformation campaigns designed to destabilize foreign governments. In the U.S., Russian disinformation has been found to manipulate political discourse, especially around the 2020 elections and ongoing racial tensions  . These disinformation campaigns can sow enough discord to lead to domestic terror incidents, as seen in 2020 with armed militias.

• Likelihood Score: 90/100

• Rationale: The extent to which state actors influence social media disinformation is substantial, and these campaigns increase the likelihood of violence, particularly in politically sensitive contexts.

Final Terrorism Risk Score (T):

T = \alpha(SES) + \beta(PI) + \gamma(IE) + \delta(SD) + \epsilon(LE) + \zeta(PSF) + \eta(EI)

Using weighted factors and scaled to 100 points:

• T = 0.15(70) + 0.2(90) + 0.2(80) + 0.1(70) + 0.1(60) + 0.15(80) + 0.2(90)

• T = 10.5 + 18 + 16 + 7 + 6 + 12 + 18 = 87.5

Conclusion:

With a terrorism risk score of 87.5/100, the likelihood of social media-driven misinformation exacerbating the current socio-political situation and contributing to a terror attack is high. Factors such as political instability, ideological extremism, and external influence have the most substantial impact on this risk, particularly in regions where these narratives are actively circulating   .