Proposed community programme
Explore the proposed programme, its evidence, and the conditions that must be met before delivery.
Data Intelligence for Communities
We help organisations use data responsibly—and help communities understand the information and technologies shaping their everyday decisions.
Proposed pilot · Fundraising in preparation
A proposed 90-day Philippine community pilot for checking information, protecting privacy, and responding safely to cyberbullying.
Practical education about misinformation and disinformation, cyberbullying and online harassment, impersonation and scams, privacy and account safety, responsible reporting, and how online influence works. The programme helps people assess information for themselves. It does not tell anyone what to believe or whom to support.
Hypothetical, everyday examples. Each maps to a planned lesson; none describes a real person or a delivered session.
The sender name looks right, the logo looks right, and the deadline is today.
Skill: verify through a channel you already trust—the number on your card or the app you installed—never the link in the message.
Thousands of shares, an angry caption, and no date or source.
Skill: find the original source and date, compare coverage, and separate the claim from the evidence before sharing.
Friends are unsure whether to reply, report, or stay silent.
Skill: preserve evidence, use trusted reporting channels, support the person targeted, and avoid public accusations.
These indicators measure different things. None of them is the share of Filipinos being manipulated, and none is a result of this programme. Each card states what was measured, why it matters here, and what someone can do next.
Why it matters here: people already want help telling real from fake, and source-checking is a teachable skill.
What someone can do next: the source-checking lesson—find the original, check the date, compare coverage.
What it does not show: the proportion of online content that is false. It measures concern, is a cross-market average, and is not a Philippines-only figure. Up 4 percentage points on the previous year.
Egan, J. (2026) ‘Overview and key findings of the 2026 Digital News Report’. Reuters Institute for the Study of Journalism, 16 June. SourceWhy it matters here: when trust in outlets is low, people need a way to judge individual claims rather than whole brands.
What someone can do next: learn to assess a specific claim and its source, and to tell reporting from opinion and advertising.
What it does not show: how accurate the news is, or that the other 72% believe misinformation. It is a survey measure of trust, down 10 percentage points on the previous year; the cross-market average is 37%.
Chua, Y.T. (2026) ‘Philippines’, Digital News Report 2026. Reuters Institute for the Study of Journalism, 16 June. SourcePractical information-literacy support ahead of the Barangay and Sangguniang Kabataan Elections currently scheduled for 2 November 2026—and beyond election day.Date check, 22 September 2026: COMELEC’s published calendar sets election day on 2 November 2026. A bill to postpone the elections to November 2028 has passed both houses of Congress and awaits the President’s decision, so the date will be rechecked before any programme material is released. The same resources apply to scams, impersonation, misleading health claims, privacy and cyberbullying whatever the election date.
Six planned service areas. Each is labelled as proposed until sessions are delivered and reported.
Guided exercises on finding an original source, checking dates, distinguishing claims from evidence and comparing coverage.
Contributions would support: facilitators, reviewed examples, English and Filipino materials, accessible formats.
Practical guidance on recognising harmful behaviour, preserving evidence, using trusted reporting channels and protecting accounts. International context: in WHO/Europe’s 2024 report on the 2021/22 school-aged children survey, 15% of adolescents aged 11, 13 and 15 across 44 countries and regions in Europe, Central Asia and Canada (more than 279,000 young people) reported being cyberbullied—not a Philippine prevalence estimate.
Contributions would support: educational sessions and maintained referral resources—not legal representation or forensic attribution.
How to verify a consequential message through an independently established channel, and the common patterns behind impersonation, fake offers and payment scams.
Contributions would support: plain-language explainers and live demonstrations.
Walkthroughs of account security and privacy settings, what to share and with whom, and how to recover a compromised account.
Contributions would support: workshop materials, translation and accessibility work.
How to document and report harmful activity to platforms and appropriate authorities without publishing accusations or exposing other people.
Contributions would support: reporting guides and maintained referral information.
How recommendations, advertising and targeting, synthetic media and coordinated activity work—and the limits of what a single post can establish.
Contributions would support: public explainers, demonstrations and independently reviewed teaching materials.
We plan to develop plain-language resources that help people recognise harmful online behaviour, preserve evidence, use trusted reporting channels, protect their accounts and privacy, and understand when to seek professional, legal or platform support.
The programme is designed around support, safety and responsible documentation—not public suspect lists or unsupported attribution.
Ninety days is a delivery plan, not a promise of a start date. The clock starts only when the start condition below is met.
Write and review the lessons, translate to Filipino, produce accessible formats, brief facilitators, agree venues with community partners.
Run community workshops, publish plain-language guides, hold human-reviewed help sessions, collect attendance and feedback.
Run knowledge checks, compile results and spending, publish the report and the reusable facilitator pack.
Start condition: the funding route is approved—processor written classification and the applicable solicitation permit or fiscal-sponsor arrangement in place—the budget is approved, and the first sponsor or contribution is confirmed.
The working budget is under approval. Amounts appear here only after approval, always as three separate figures so planned allocation, money received and actual expenditure are never confused.
| Category | Planned | Received | Spent |
|---|---|---|---|
| Learning materials and translation | Pending approval | — | — |
| Workshops and facilitation | Pending approval | — | — |
| Accessibility and formats | Pending approval | — | — |
| Evaluation and public reporting | Pending approval | — | — |
| Administration and payment fees | Pending approval | — | — |
Published before any contribution is requested, and kept current for the life of the programme.
InsightPulseAI, the trading brand of Dataverse IT Consultancy, a DTI-registered sole proprietorship in Makati City. The programme is not a separate registered charity and does not claim non-profit status.
Two routes are being evaluated: a registered non-profit fiscal sponsor that holds the solicitation permit and receives contributions, or invoiced sponsorship packages and paid workshop seats collected on the operator’s existing payment account.
The chosen route, the processor’s written classification and the applicable permit will be published here before any contribution is requested.
Resources produced, sessions delivered, attendance, completion, participant feedback, knowledge-check results and spending—reported in aggregate, and separately from any research statistic quoted on this page.
Contributor, participant, assistance-request and commercial-marketing data are kept separate. No political preferences are collected, no assistance request is used for advertising, and no identifiable cyberbullying submission is published.
Nonpartisan: no endorsements, no voting guidance, no political profiles.
Tell us about your school, barangay, workplace or community group. This is a request for information, not a booking, and no payment is involved.
Seeing content, changing an attitude, changing a vote and changing an election result are different outcomes. Evidence for one does not automatically establish the others.
Neither study cited above establishes that a Philippine election outcome was caused by a particular feed, advertiser or technology. InsightPulseAI does not claim to identify who “controlled” an election. Teaching modules will use documented examples with sources and dates, never allegations about unidentified people.Documented mechanisms and their limits. The purpose is to explain what researchers measured, where findings differ and what remains uncertain—not to claim that any technology decided an election.
Feed-ranking systems change which content people encounter. Experimental studies have found different effects on political attitudes across platforms and settings. A seven-week 2023 experiment with about 4,965 US users of X found that the algorithmic feed shifted some measured attitudes, while neither switching the algorithm on nor off significantly affected affective polarisation or self-reported partisanship. A three-month experiment during the 2020 US election found that chronological feeds on Facebook and Instagram substantially changed time on platform and content exposure but did not significantly alter issue polarisation, affective polarisation, political knowledge or other key attitudes.
Understand the difference between followed content, recommended content and paid placements; consult original sources outside a single feed.
Political microtargeting selects audiences using data and delivers different messages to different groups, rather than broadcasting one message to everyone. Targeting is a distribution mechanism; it says nothing by itself about whether a message is true.
Examine the advertiser, the sponsorship disclosure, the claim and the evidence. Treat targeting as how a message reached you—not as a verdict on its truthfulness.
The Oxford Internet Institute’s 2020 inventory, published in 2021, documented organised social media manipulation in 81 countries, involving governments, political parties and private firms. It is a historical research inventory—not a 2026 count, and not evidence that election results were changed.
Bradshaw, Bailey and Howard (2021) Oxford Internet Institute
Distinguish visible repetition from evidence of coordination. Popularity, similar wording or multiple Pages are not proof of common ownership.
In May 2024 the US Federal Communications Commission proposed a US$6 million fine over robocalls made two days before the January 2024 New Hampshire primary that used an AI-generated clone of President Biden’s voice with spoofed caller ID; the fine was issued in September 2024. A single documented US case, used here to illustrate the mechanism.
Federal Communications Commission (2024) Proposed fine, 23 May 2024 · Fine issued, September 2024
Verify consequential messages through an independently established channel rather than trusting a convincing voice or a familiar-looking identity.