Exploring with False Information

Innovation methods

Data sources

SDG(s)

Sustainable Development Goal(s)

9Industry, innovation and infrastructure

16Peace, justice and strong institutions

Disclaimer:

Please be aware that the content herein has not been peer reviewed. It consists of personal reflections, insights, and learnings of the contributor(s). It may not be exhaustive, nor does it aim to be authoritative knowledge.

Learnings on your challenge

What are the top key insights you generated about your learning challenge during this Action Learning Plan? (Please list a maximum of 5 key insights)

1. False information poses a significant challenge in Jordan, with various motivations for sharing false or unverified information on social media platforms. 2. The consequences of false Information can be real and far-reaching, as seen in the case of vaccination hesitancy in Jordan, and it erodes trust in reliable sources and institutions. 3. Addressing false information requires a multifaceted approach, including research, data analysis, and tailored solutions that consider local contexts and sentiments. 4. The UNDP Jordan Accelerator Lab is actively working on innovative solutions to combat false information, utilizing machine learning, primary research, and other methods to better understand and address the issue in Jordan.

Considering the outcomes of this learning challenge, which of the following best describe the handover process? (Please select all that apply)

Our work has not yet scaled, Other

Can you provide more detail on your handover process?

We collected and analysed more than 500 K tweets from Jordan using an AI model to categorise the data into topics and label them if they had misinformation, hate speech, or luring. We are currently doing a manual check on a sample of the tweets to test the analysis and get a thematic analysis of the tweets. You can check the dashboard here https://public.tableau.com/app/profile/ahmad.amaireh5395/viz/Twitter_17183022315540/_1AboutData?publish=yes

Please paste any link(s) to blog(s) or publication(s) that articulate the learnings on your frontier challenge.

https://www.undp.org/jordan/blog/unravelling-web-false-information-jordan

Data and Methods

Relating to your types of data, why did you chose these? What gaps in available data were these addressing?

We are exploring different data sets of false information that includes utilizing Ai algorithms to identify patterns of false information on Twitter. We are also using qualitative surveys to capture insights on false information in different locations in the country.

Why was it necessary to apply the above innovation method on your frontier challenge? How did these help you to unpack the system?

Our method involves a combination of AI analysis, manual thematic analysis, and primary research, allowing for a holistic understanding of the false information landscape. This approach considers the quantitative aspects (data-driven analysis) and qualitative aspects (insights from primary research), providing a more nuanced and comprehensive perspective.

Partners

Please indicate what partners you have actually worked with for this learning challenge.

Please state the name of the partner:

What sector does your partner belong to?

United Nations

Please provide a brief description of the partnership.

Is this a new and unusual partner for UNDP?

End

Bonus question: How did the interplay of innovation methods, new forms of data and unusual partners enable you to learn & generate insights, that otherwise you would have not been able to achieve?

You can check the dashboard for now https://public.tableau.com/app/profile/ahmad.amaireh5395/viz/Twitter_17183022315540/_1AboutData?publish=yes

Please upload any further supporting evidence / documents / data you have produced on your frontier challenge that showcase your learnings.

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