Turn social market commentary into structured investor sentiment data
StockTwits gives investors and traders a real-time social layer for market commentary. Responsly adds structured survey instruments to that social signal — converting crowd noise into quantifiable sentiment data with granularity that bullish/bearish tags alone cannot provide.
For fintech teams, community managers, and market researchers working with StockTwits audiences, this integration creates a feedback channel that captures the reasoning behind market opinions. Every earnings confidence survey, platform satisfaction questionnaire, and content preference poll produces data you can analyze statistically rather than scroll through anecdotally.
Why social sentiment alone leaves gaps
StockTwits streams millions of posts tagged bullish or bearish. That binary signal is useful directional data, but it misses nuance. A trader posting bearish on $TSLA might be hedging a long position, reacting to a single headline, or expressing a year-long thesis. The tag doesn’t distinguish between them.
Structured surveys fill this gap:
- confidence scales (1–10) capture intensity, not just direction,
- holding-period questions reveal whether sentiment reflects a day trade or a conviction position,
- open-ended prompts surface the specific catalysts driving an opinion,
- and demographic questions (experience level, portfolio size) let you weight responses by sophistication.
When you layer survey data over social sentiment, the resulting picture is richer — and more actionable for product, content, and research decisions. For background on capturing nuanced customer perspectives, see what is customer satisfaction.
Use case: earnings season confidence surveys
A fintech research team surveys their StockTwits community before and after major earnings announcements. The pre-earnings survey asks: confidence level (1–10), expected earnings direction, and primary concern about the quarter.
Post-earnings:
- the team compares pre-earnings confidence with actual post-announcement sentiment shifts,
- tickers where community confidence diverged most from results are flagged for deeper analysis,
- a research report published to the community showed that tickers where pre-earnings confidence exceeded 8.0 beat consensus estimates 64% of the time across three quarters of data.
The survey data transformed subjective social commentary into a quantitative signal with trackable accuracy. Explore strategies for closing the loop on collected feedback in our closed feedback loop guide.
Use case: trading platform satisfaction for fintech products
A fintech startup uses StockTwits to reach active traders and runs quarterly platform satisfaction surveys. Questions cover execution speed, charting tools, mobile experience, and customer support.
Results after two survey cycles:
- charting tools scored 3.2 out of 5 — the lowest category — and open-ended responses pointed to missing drawing tools and slow indicator loading,
- the product team prioritized charting improvements and re-surveyed after the release,
- post-update scores rose to 4.1, and the StockTwits community organically posted about the improvements, creating a measurable social lift alongside the survey data.
Use case: community content preference surveys
A financial media company surveys its StockTwits followers: what content formats do you value most — market recaps, deep-dive analyses, video commentary, or earnings previews?
The results reshaped their editorial calendar:
- 58% of respondents ranked earnings previews as their top preference, but only 15% of published content was earnings-focused,
- the team shifted production to double earnings preview output,
- newsletter open rates climbed from 22% to 31% within six weeks as content matched stated preferences.
Use skip logic to adapt follow-up questions based on format preference — video fans see questions about length and frequency while readers see questions about depth and sourcing.
Use case: event-driven market opinion capture
During a flash crash, a market research firm deploys a rapid-response survey to its StockTwits panel within two hours: “What is your immediate reaction?” (panic, buying opportunity, no change to thesis), “Have you adjusted positions?”, and “What is your 30-day outlook?”
The data collected:
- 67% of respondents viewed the crash as a buying opportunity, contradicting the overwhelmingly bearish social stream,
- 41% had already added to positions before the survey closed,
- the firm published the structured results alongside the raw social sentiment, and the contrast became one of their most-shared research pieces of the quarter.
What data flows between Responsly and StockTwits
Each survey response is captured with:
- numerical ratings (confidence scores, satisfaction scales) as structured data points,
- multiple-choice selections (investor type, preferred content, position action) as categorical data,
- open-ended text responses as qualitative data for thematic analysis,
- ticker and event parameters appended to the survey URL as automatic segment tags,
- and timestamp data for time-series analysis around market events.
Start capturing structured investor sentiment
Connect Responsly to your StockTwits community, launch your first earnings confidence survey, and see what your audience really thinks — with data you can measure, segment, and track over time.
