text: Biases can be encoded in ways that form a continuum from sub- 
  tle patterns like referring to women doctors as if doctor itself entails 
  not-woman or referring to both genders excluding the possibility of 
  non-binary gender identities, through directly contested framings 
  (e.g. undocumented immigrants vs. illegal immigrants or illegals), to 
  language that is widely recognized to be derogatory (e.g. racial slurs) 
  yet still used by some. While some of the most overtly derogatory 
  words could be filtered out, not all forms of online abuse are easily 
  detectable using such taboo words, as evidenced by the growing 
  body of research on online abuse detection [45, 109]. Furthermore, 
  in addition to abusive language [139] and hate speech [67], there 
  are subtler forms of negativity such as gender bias [137], microag- 
  gressions [22], dehumanization [83], and various socio-political 
  framing biases [44, 114] that are prevalent in language data. For 
  example, describing a woman’s account of her experience of sexism 
  with the word tantrum both reflects a worldview where the sexist 
  actions are normative and foregrounds a stereotype of women as 
  childish and not in control of their emotions. 