StockTwits social-media messages (licensed)

Licensed: not exercised here

StockTwits message data is a paid licensed product (StockTwits Inc., often via Social Market Analytics), so it carries no provenance badge: the access path below was not run in this session. Treat it as unverified until someone exercises it through a StockTwits or SMA data licence. This is the honest grade under the institute’s Verified discipline.

StockTwits is a finance-focused social network on which users post short messages tagged with stock tickers ($AAPL), frequently attaching a self-declared Bullish or Bearish label. That explicit sentiment tag, plus the ticker structure, makes it a widely used measure of retail investor sentiment at the firm-day level for text-as-data research. A paper we distill uses it: Cookson, Niessner & Schiller use roughly 260 million StockTwits posts (January 2010 to December 2021) matched to 6,438 U.S. M&A deals to build firm-specific abnormal sentiment after a merger announcement, showing that negative social-media sentiment predicts deal withdrawal.

• Cost: licensed. Some current data is reachable through StockTwits’ own API under terms, but the historical research corpus is typically obtained through Social Market Analytics or a direct StockTwits data agreement.
• Vendor: StockTwits Inc.; redistribution and history often via Social Market Analytics (SMA).
• Coverage: message-level, ticker-tagged, from the platform’s launch around 2009 to present; deepest for liquid U.S. equities that attract retail attention.

Access (when licensed)

• Through a StockTwits or SMA data licence. The research corpus (full message history with timestamps, tickers, and the Bullish/Bearish label) comes as bulk extracts under agreement; the public API is rate-limited and governed by terms that restrict bulk historical collection.
• The grain is the message. Each row is a post with author, timestamp, ticker(s), text, and the optional sentiment tag; firm-day sentiment measures are aggregated up from messages, as the citing paper’s abnormal-sentiment measure is.
• Credentials are required. Keep any credentials in .env, never hard-coded.

Gotchas (the ones that bite pipelines)

These are the failure modes to expect; they are documented, not verified here.

• The self-labeled sentiment tag is optional and sparse. Only a fraction of messages carry an explicit Bullish/Bearish flag; the rest need a text classifier, and the labeled subset is not a random sample of opinion. State how unlabeled messages are handled.
• Population is self-selected retail, not the market. Posters skew toward active retail traders and toward high-attention tickers; coverage is thin for small or neglected stocks and absent for most of the cross section. Sentiment is informative about the attentive crowd, not the marginal investor.
• Volume confounds sentiment. Message counts spike around news, so an abnormal-sentiment measure must net out the attention spike; the citing paper builds an abnormal sentiment for exactly this reason. Raw sentiment tracks volume.
• Bots, promotion, and manipulation. Ticker streams contain spam, paid promotion, and coordinated posting; naive aggregation imports that noise. Filter duplicates and low-quality accounts.
• Terms restrict bulk reuse. Both the API and the licensed corpus carry redistribution limits; cache derived features (firm-day sentiment), not raw messages, and do not republish the text. The same firm-day measure built from different filters will not match another paper’s; document the pipeline.

Citation

Cite the source and provider, e.g.: StockTwits messages (StockTwits Inc., via Social Market Analytics), [dates], accessed YYYY-MM-DD. State the message count and date range, how sentiment was assigned (self-label versus classifier), and how attention/volume was netted out.