Profanity on Twitter in the Nordics

Steven Coats
English Philology, University of Oulu
steven.coats@oulu.fi

Higher Seminar Series, Södertörns Högskola
January 18th, 2018

Background and research questions

Go to my homepage cc.oulu.fi/~scoats to follow these slides (explore the interactive elements!)

Online language and gender: Small but significant differences in feature frequencies have been observed (e.g. Argamon et al. 2007, Bamman et al. 2014, Coats 2016)

English profanity: Males use more overall (Mehl & Pennebaker 2003) and on Twitter (Bamman et al. 2014, Wang et al. 2014); males use stronger profanities (McEnery 2006)
How is profanity (English and Nordic-language) used on Twitter in the Nordic countries?
Goals are to:

Explore extent of profanity use by gender
Quantify results by country and gender and identify most characteristic words
Use word embeddings to investigate semantic space by gender

Data collection | Twitter Streaming API

627m tweets with place attributes collected globally from the Twitter Streaming API 9 November 2016 – 5 June 2017 using Tweepy (Roesslein 2015)
From these “seed” tweets, identify users from Nordic countries, disambiguate gender, then download user timelimes (from REST API)

Data collection | Location disambiguation

Tweets can have three distinct location metadata values
The user location is a field where many users write their home location (although they don’t have to)
The geo field contains exact lat-long coordinates (if a user enabled GPS)
The place field optionally tags a tweet to a place
BUT user location can be any character string, geo changes when people move around, place can indicate a user went to a place, but also that he/she is tweeting about a place

Data collection | Location disambiguation 2

More accurate geolocation: Consider the metadata from both user location and place fields
1,627 strings corresponding to Nordic place names (cities, regions, country names) were matched with the user location attribute
Alingsås, Alvesta, Aneby, Arboga, Arjeplog, Arvidsjaur, Arvika, Askersund (etc.)
If the most frequent country within the place value for all tweets from that user matched the same country as the country of the user location, user was retained in the data and assigned to that country

Data collection | Gender disambiguation

Twitter users not required to specify gender (no attribute in profile provided for gender)
Tweets have a metadata field in which many users enter their real names, although it is not required (e.g. “Anna Hansson”)

Nordic Name Genderizer: Name frequency information for 15,406 names given to males and 17,856 names given to females obtained from Nordic statistical offices and used to induce a probability distribution for name gender

\[ \small P(name_x \in male) = \frac{\sum{name_x \in male}}{\sum{name_x}} ,\qquad P(name_x \in female) = \frac{\sum{name_x \in female}}{\sum{name_x}} \normalsize \]

author_name entity like “Anna Hansson” -> user classified as female
author_name entities such as = “swedengirl123”, “!!!!ROCKER!!!!” -> ignored

Data collection | Twitter REST API

Timelines (up to 3,250 tweets) were downloaded from the Twitter REST API for users who could be assigned to a Nordic country/territory and whose gender (male/female) could be inferred
Final data for analysis consists of 30,966,040 tweets by 36,001 users comprising 393,304,865 word tokens

Some Sample Tweets containing Profanity

Profanity lists

Lexical-item-based approach
924 potentially offensive English words (with optional plural ending)

“Core” profanities such as shit, fuck, piss, cunt, damn, others such as bollocks, faggot, fuq, heeb, hell, hillbilly, homo, honkey, hussy, jackass, jackoff, jigaboo, lameass, lardass, lesbo, lezzie, limey, limpdick, mcfagget, minge, mooncricket, nigger, paki, pansy, peckerhead, pikey, piss, pussy, spastic, snownigger, twat, whitetrash, wtf, etc.

1,602 potentially offensive Nordic words