Social ListeningAutomation
Know what people are saying about your brand and your category as it happens, with noise filtered out and the important threads summarised for you.
Social Listening Automation: what the work involves
Brand mentions are scattered across Facebook groups, X posts, YouTube comments, Reddit threads, news sites and review pages. Marketing teams check them sporadically, which means a complaint that is gaining traction is found after it has become a story. Manual searches also miss mixed-language posts and shorthand, and nobody has time to read a thousand mentions and say what changed.
DevKey builds a listening pipeline that collects mentions from the sources you can legitimately access, through official APIs, licensed data providers and public feeds. Posts are deduplicated, language-detected and classified by topic, sentiment and relevance, so spam and unrelated usage drop out. Models cluster mentions into themes and write a short briefing on what is rising. Alerts fire on defined triggers, such as a spike, an influencer mention or a safety complaint, and are sent to the team in Slack or email.
Core features
Multi-source collection
Official APIs, licensed data and RSS feeds bring in mentions of brand names, products, executives and competitors.
Relevance filtering
Keyword matches are checked for context, so a mention of an apple the fruit does not pollute the brand feed.
Sentiment and emotion tagging
Posts in English, Urdu and Roman Urdu are scored, with sarcasm and mixed tone treated as uncertain and sampled for review.
Theme clustering and briefings
Similar mentions are grouped into topics, and a periodic summary describes what is growing, with example posts linked.
Spike and keyword alerts
Rapid increases, crisis terms or mentions by high-reach accounts trigger an alert with the evidence and a suggested owner.
Competitor comparison
Share of conversation and topic mix are compared across your brand and chosen competitors over time.
What we get right before launch
Platform access and rules
Major platforms restrict scraping and have changed API access repeatedly. We design around official or licensed sources, tell you what coverage is realistic, and avoid collecting private groups or personal profiles.
Sentiment is blunt
Automated sentiment misreads irony, slang and mixed-language posts. We validate on a labelled sample from your own channels, report the error rate, and use sentiment as a trend signal and not as a verdict on an individual post.
Alert fatigue
Constant alerts get ignored. We begin with narrow triggers, measure how many led to action, and tune thresholds until the team trusts them.
Tools and technology
- Python
- OpenAI GPT
- Anthropic Claude
- BERTopic
- Meta and YouTube APIs
- PostgreSQL
- OpenSearch
- Slack API
- n8n
Common questions, answered
Which platforms can you monitor?
Anything with an official API, licensed feed or public page we can lawfully read, such as news, YouTube, public pages and review sites. Coverage of some networks is limited, and we state this before we begin.
Does it understand Roman Urdu?
Reasonably for common phrases, but accuracy is lower than English. We test on posts about your brand, build a glossary of local slang and keep humans in the loop for borderline cases.
Can it detect fake news or bots?
Not reliably. It can flag unusual posting patterns or coordinated spikes for investigation, but determining intent or truth requires human analysis and often external verification.
How is this different from a social media management tool?
Management tools publish and respond. Listening focuses on collecting and analysing what people say across the web, including places where your brand does not post, and turning that into insight and alerts.
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Ready to start your Social Listening Automation project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.