Conceptual metaphor

Overview

A conceptual metaphor maps a familiar domain onto a less familiar one, for example describing an illness as an enemy to be fought. Corpus studies ask which source domains appear for a given target, how often, and in which contexts. Only one paper card in the wiki covers this concept, so this page describes that study's approach and the choices it makes. The shared core is to define each frame by a set of words, count the texts that use them, and compare the counts with a literal baseline 2004.06986 p. 17-18.

Variants

Lexicon-presence counting

Build one word list per frame, then count the texts that contain at least one word from the list. The authors rank each list by usage and compare only the top 30 or top 50 units, so that frames with different list sizes can be compared 2004.06986 p. 18. A literal control frame (FAMILY) is counted the same way. Reported shares on the authors' corpus: WAR 4.96% at 30 units and 5.25% at 50 units, against 11.91% and 12.06% for the control 2004.06986 tab. 2. Card: Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter.

Topic-conditioned frame use

The same study trains an LDA topic model on the corpus and then averages the topic probabilities of the tweets that match a frame. This shows which discussion topics the frame sits in. In the 4-topic model, WAR tweets have their highest average probability in the topic the authors label reacting to the epidemic, and the lowest in community and social compassion 2004.06986 p. 13-15. The topic labels are the authors' reading of the top words, and the topics change between runs because no seed is given 2004.06986 p. 10, p. 15.

Manual metaphor identification (not used)

The paper names MIPVU as the reference procedure for identifying metaphorical language, but does not apply it. It checked a subsample by hand, accepted that some matches are literal, and states that a full MIPVU check would change the WAR share 2004.06986 p. 13. No card in the wiki reports a MIPVU run.

When to use which

Implementation sketch

The shape of lexicon-presence counting, adapted from the study's description 2004.06986 p. 17-18. The paper gives no code.

# Share of texts using each frame, at a fixed lexicon size  [p. 17-18]
# Input:  texts (sets of tokens), frames (dict name -> list of units)
# Output: dict name -> share of all texts

def frame_shares(texts, frames, cutoff):
    shares = {}
    for name, units in frames.items():
        # Rank units by how often they occur in the corpus  [p. 18]
        ranked = sorted(units, key=lambda u: -occurrences(texts, u))
        top = set(ranked[:cutoff])
        # Count texts that contain at least one unit from the top list  [p. 17]
        hits = sum(1 for t in texts if t & top)
        shares[name] = hits / len(texts)
    return shares

Where implementations differ: the study does not say whether frequency means texts or token occurrences, and a rebuild should choose one and report it 2004.06986 p. 18.

Contradictions

None between cards. A cross-corpus difference: on the authors' 2-week corpus WAR is 5.32% of tweets, while on the Lamsal 2-week dataset it is 6.94%, and FAMILY falls from 12.06% to 8.60% 2004.06986 tab. 3. The authors put most of this down to keyword choice, since the Lamsal dataset includes the keyword "corona", which the authors excluded 2004.06986 p. 20. Both figures are reported as they appear in the study.

Papers

• Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter: lexicon counts of war, monster, storm and tsunami frames against a literal control, with topic placement for war tweets.