docs/ethical_considerations.md

Ethical considerations: CodeMixCheck v2.2.1

Synthetic misinformation about real places

Synthetic claims combine real Tamil Nadu districts, cities, festivals and institution types with fictional events, for example a parasite found in a named city's tap water, a festival "cancelled", or a "scheme" discontinued. A label such as False or True describes the simulated world. Risks:

• A record taken out of context could circulate as real (mis)information.
• A model trained on the data could associate real place names with fabricated events.

Mitigations: the dataset card states prominently that labels are simulated, and lists real-world fact-checking and statements about real entities as out-of-scope uses. Do not quote individual records out of context.

Privacy

• Public files: the automated screen found no URLs, e-mail addresses or phone numbers. The @ handles in the text are 11 template placeholders (for example @someone, @localnews_tn, @amma and typo variants). Generic placeholders like these could coincide with real accounts. They do not refer to anyone.
• Unreleased social-media comments: these are personal expression by third parties. Some address or name individuals (first names or initials), and two contain strong profanity. They are not included in this release.

Offensive content

abuse_detection contains Tanglish insults and slurs, some masked with emoji (🖕). Some sentiment rows are hostile. The content is short and synthetic, but readers may find it offensive.

Representation

The generator encodes one author's view of how misinformation, hearsay and sentiment are expressed in Tanglish. Sentiment and abuse conventions (for example, "poda dei" read as playful) are culturally situated and can be contested.

Dual use

A corpus of realistic code-mixed misinformation templates could help someone generate convincing rumours. The data is low in uplift (short, templated and clearly labelled), but users must not train generators to produce realistic misinformation (see the out-of-scope uses in the dataset card).

Annotator welfare and compensation

All labelling was done by the dataset author. No crowdworkers were involved.