The gender the emoji in a text column depict

emoji_gender() counts, per row, the person emoji by the gender they are drawn with. This is the gender a glyph depicts, not the writer's and not the reader's; see Glyphs, not people in tidyEmoji. Studies of the gender of the people sending emoji, such as Lin (2025), ask a different question, which this verb does not answer.

Usage

emoji_gender(data, text, presentation = "emoji")

Arguments

data

A data frame or tibble containing a text column. Grouped data frames are accepted. The verbs that work a row at a time (adding columns, or keeping and expanding rows) carry the grouping through to their result, as dplyr::mutate() and dplyr::filter() do. The verbs that pool across rows – the counts, the co-occurrence edge lists, the time series – warn that they ignore the grouping and return one corpus-wide answer.

text

The text column to scan, supplied unquoted. Any atomic column is accepted and read as character, so a factor works and a numeric, Date or logical one simply contains no emoji. A list column – or a data-frame column – is refused rather than coerced, because coercing one deparses it and the emoji found would be in the code rather than in your data. What counts as an emoji is the same in every verb; see the Detection section of tidyEmoji for the one case that surprises people, code points that are emoji only when they carry U+FE0F.

presentation

Which code points count as emoji: "emoji" (the default) or "any".

• "emoji" requires emoji presentation, so U+2764 U+FE0F is an emoji and the bare U+2764 is not. This is the default and it will stay the default: widening it would silently recount every corpus already analysed.

• "any" also reads the text-presentation spellings Unicode lists for an emoji (the bare heart, U+263A, U+2714, a keycap without its selector), because several keyboards emit them. It never reads the copyright, registered and trade-mark signs (U+00A9, U+00AE, U+2122), which are typography far more often than they are emoji.

"any" only ever adds occurrences: everything "emoji" finds, it finds too, in the same place. emoji_coverage() reports how many it adds and how many of those a lexicon can score, which is the cost of the choice in one number. Every verb that counts, scores or rewrites the emoji in a text column takes this argument, so counts still agree across the package for whichever value you use. The flag verbs do not need it, since a flag has no text-presentation form.

Value

data, as a tibble, with added columns .emoji_n_gender_variable (occurrences of a person emoji whose family has more than one gender form), .emoji_n_female, .emoji_n_male, .emoji_n_neutral and .emoji_n_mixed (occurrences by the gender they depict) and .emoji_gender (the row's one gender, "mixed" when its emoji differ, or NA when none depicts a gender). A grouped input stays grouped.

Details

The classification is emoji_decompose()'s: the female and male signs, the gendered people (woman, man, girl, boy, old woman, old man), and a list of twelve emoji whose gender lives in a single code point, such as prince and princess. "mixed" is a multi-person emoji showing both, a couple or a family, and is this package's own derivation. "neutral" is Unicode's gender-neutral form, which platforms draw differently.

Gender-variable emoji are the ones whose catalogue family has more than one gender form: person, woman and man running, or the prince, the princess and the person with crown. The families are derived from the catalogue, by grouping every emoji under its gender-neutral form, not listed by hand. .emoji_n_gender_variable is the denominator for "how often did writers pick a gendered form": the woman dancing has no other form and so is female without being a choice.

References

Lin, Z.-X. (2025). Exploring gender differences in emoji usage: implications for human-computer interaction. Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP), 274-282.

See also

emoji_decompose(); emoji_skin_tone() for tones.

Examples

d <- data.frame(text = c("\U0001f3c3\u200d\u2640\ufe0f and \U0001f3c3",
                         "\U0001f483 \U0001f57a",
                         "no people \U0001f600"))
emoji_gender(d, text)
#> # A tibble: 3 × 7
#>   text     .emoji_n_gender_vari…¹ .emoji_n_female .emoji_n_male .emoji_n_neutral
#>   <chr>                     <int>           <int>         <int>            <int>
#> 1 🏃‍♀️ and 🏃…                      2               1             0                1
#> 2 💃 🕺                         0               1             1                0
#> 3 no peop…                      0               0             0                0
#> # ℹ abbreviated name: ¹​.emoji_n_gender_variable
#> # ℹ 2 more variables: .emoji_n_mixed <int>, .emoji_gender <chr>