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Please be aware that the content herein has not been peer reviewed.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.
Overview
Prepared by (Name of the experimenter)
Javier Brolo and María Inés Castañeda
On date (Day/Month/Year)
August 20th, 2021
Current status of experimental activity
Completed
What portfolio does this activity correspond to? If any
Knowledge management
What is the frontier challenge does this activity responds to?
How to base decisions and behaviors on existing knowledge
What is the learning question(from your action learning plan) is this activity related to?
What makes it easier for decisions and behaviors to be informed by existing knowledge rather than starting from scratch?
Please categorize the type that best identifies this experimental activity:
Pre Experimental (trial and error, prototype, a/b testing), Quasi Experimental (Analytical, observations, etc)
Which sector are you partnering with for this activity? Please select all that apply
Civil Society/ NGOs
Please list the names of partners mentioned in the previous question:
Centro de Voluntariado Guatemalteco (CVG), and other volunteer organizations
Design
What is the specific learning intent of the activity?
The learning intents from this
activity are twofold. On the one hand, building on the capacity developed
earlier to analyze conversations in social media around a hashtag, we wanted to
test the concept of using twitter to generate collective intelligence with the
help of volunteer organizations. On the other hand, from the collective
intelligence generated we want to learn in which ways people see that it's
possible to improve wellbeing and at the same time protect nature.
What is your hypothesis? IF... THEN....
IF we volunteers collect data
about nature-based solutions in twitter, THEN we can use data analysis
techniques to generate collective intelligence.
Does the activity use a control group for comparison?
No, it does not use a control group
How is the intervention assigned to different groups in your experiment?
Other
Describe which actions will you take to test your hypothesis:
Before launching the project, a
brief proposal explaining the challenge was written and sent to the
environmental program officer in the UNDP country office to validate. Then we
reached out to our strategic partner in civil society (Centro de Voluntariado Guatemalteco), requesting their collaboration. CVG helped recruiting
volunteers, and we ran an initial pilot to test the instructions to participate
through a WhatsApp group. With this, we refined the instructions and launched
an open social media campaign with the hashtag #DesarrolloConNaturaleza, which
was led by volunteers and requested people to post photos of examples of things
that help improve people's economic and social wellbeing and at the same time
protect or repair damage in nature. Data was monitored daily from the Twitter
API using the software R. We ran a couple of complementary workshops with
volunteers to increase the content generated. Afterwards, we used ShinyApps.io
to publish a library of the data collected.
What is the unit of analysis of this experimental activity?
There are two levels of analysis, the campaign as a whole, and the individual tweets generated.
Please describe the data collection technique proposed
We collected data from the Twitter API
What is the timeline of the experimental activity? (Months/Days)
About a month
What is the estimated sample size?
100-999
What is the total estimated monetary resources needed for this experiment?
Less than 1,000 USD
Quality Check
This activity is relevant to a CPD outcome, The hypothesis is clearly stated, This activity offers strong collaboration oportunities, This activity offers a high potential for scaling, This activity has a low risk
Please upload any supporting images or visuals for this experiment.
Please upload any supporting links
What are the estimated non- monetary resources required for this experiment? (time allocation from team, external resources, etc) If any.
To run the experiments, we need
expertise in the collection and analysis of social media data. Also, we used
the open-source software R. We also needed time to organize the workshops with
volunteers, and time to collect the observations. All the exchanges were done
remotely so we needed Zoom and Mural to coordinate. We also needed Twitter
accounts and a Twitter developer account to set up the API communication.
Results
Was the original hypothesis (If.. then) proven or disproven?
Partially proven
Do you have observations about the methodology chosen for the experiment? What would you change?
It was difficult to gain access
to the location of the posts, because of user's privacy. Using text within the
tweet makes it easier to parse and analyze later, but it can be messy, with
errors, and requires training. Young people in Guatemala are less enthusiastic
about the use of Twitter. It is the easiest platform for data analysis in terms
of accessibility. However, people prefer Instagram or Tik Tok for sharing
content.
From design to results, how long did this activity take? (Time in months)
About a month
What were the actual monetary resources invested in this activity? (Amount in USD)
No additional costs to normal operations. Nothing was needed to be procured or bought as we used open source data and software.
Does this activity have a follow up or a next stage? Please explain
Yes. The next stage is to transfer the capacity to use these tools. However, it has proven difficult to implement due to lack of people with required skills.
Is this experiment planned to scale? How? With whom?
Yes. It is planned that collective intelligence methodologies using social media data be scales to other topics within the country office.
Please include any supporting images that could be used to showcase this activity
Please add any supporting links that describe the planning, implementation, results of learning of this activity? For example a tweet, a blog, or a report.
Considering the outcomes of this experimental activity, which of the following best describe what happened after? (Please select all that apply)
Solutions tested in this experiment were scaled in numbers, This experiment led to partnerships
Learning
What do you know now about the action plan learning question that you did not know before? What were your main learnings during this experiment?
There were many learnings from
this activity. Regarding nature-based solutions, we identified several themes
where grassroots solutions are abundant: (1) waste management including
plastics; (2) production and consumption; (3) tourism; (4) community projects;
(5) mobilization; (6) environmental protection; (7) food; (8) household habits;
(9) sharing time with others; (10) use of water. We also learned that Twitter
is a viable way to generate collective intelligence using citizen generated data,
although Twitter is not the preferred platform for users. We also learned that
showing that something works is not enough to transfer capacity, because people
see a product, they are interested in not with the intention of doing it
themselves, but something to be done for them.
What were the main obstacles and challenges you encountered during this activity?
Several challenges. Some included that it's not easy to gain access to location data from twitter (as it's not default). Also, Twitter is not the preferred platform for young people in Guatemala. Moreover, it's difficult to find people with the technical skills to replicate the exercise.
Who at UNDP might benefit from the results of this experimental activity? Why?
All the programmatic areas within
the country office, as well as the strategic unit. Social media data is an
abundant source of citizen-generated data that can help understand perspectives
from the conversations of everyday people, and this can be considered for
making decisions.
Who outside UNDP might benefit from the results of this experiment? and why?
Volunteer organizations that want
to generate collective intelligence, as well as other civil society
organizations. Government agencies that are looking for low-cost ways to learn
from the perspectives of citizens.
Did this experiment require iterations? If so, how many and what did you change/adjust along the way? and why?
This was the second iteration to develop the social media analysis, now it included some automations and public access to analysis in (close to) real time.
What advice would you give someone wanting to replicate this experimental activity?
See if there are conditions to use instagram, try to get location data turned on by users, be aware that it requires technical skills to access social media data.
Can this experiment be replicated in another thematic area or other SDGs? If yes, what would need to be considered, if no, why not?
Absolutely, it's mostly a test on a methodology that is agnostic topic-wise.
How much the "sense" and "explore" phases of the learning cycle influenced/shaped this experiment? In hindsight, what would you have done differently with your fellow Solution Mapper and Explorer?
Sense helped narrow down the topic to nature-based solutions, exploration initiated the interest in the use of social media data as a source of information.
What surprised you?
There is a large demand to take
advantage of social media data, but projects are not looking for in house
solutions but rather readymade commercial services that can be purchased.
Capacity building within projects is difficult, because people are recruited
with product-oriented responsibilities, rather than for problem solving skills.
Waste management is one of the most accessible ways to improve economic and
social wellbeing, when at the same time protection or repairing damage on
nature.
11Sustainable cities and communities
13Climate action
17Partnerships for the goals
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