Metaphors we read by: Finding metaphorical conceptualizations of reading in web 2.0
While interdisciplinary research on metaphor is abundant (Eggs, 2000; Semino & Demjén, 2017;
Veale et al., 2016), it is still scarce in Digital Humanities. At the intersection of literary studies,
corpus stylistics, and digital humanities, we present an exploratory quantitative metaphor
analysis of a corpus of German language lay book reviews. Using a deliberately simple
methodological approach that operates on seed words for conceptual sources and targets we
investigate how reading experiences of literary texts are metaphorically presented by reviewers.
We explore a corpus of approx. 1.3 mill. book reviews for metaphors used to conceptualize the
target domain READING EXPERIENCE.
In line with conceptual metaphor theory, metaphors in language are understood as closely
linked to human thought processes and experiences (Lakoff & Johnson, 1980, pp. 4–6;
Shutova, 2017). They are mappings from typically more basic experiential source domains
(LIFE) to more abstract target domains (READING EXPERIENCE), indicated by indirectly used
lexis (the words come, end, and road in “we've come to the end of our road”, VUAMC, Steen et
Starting from findings on literature reviews in English (Stockwell, 2009; Nuttall & Harrison, 2018)
and on reviews in German (Köhler, 1999), we analyze metaphor patterns in social reading
networks, with a particular focus on the mapping READING EXPERIENCE IS MOTION. The
main aim at this stage is to draw up a first typology of mappings.
Method and Data
In view of the challenges of reliable automatic metaphor detection (Veale et al., 2016), we apply
a deliberately simple rule-based corpus stylistic approach (Deignan & Semino, 2010). A
commonly used resource for identification of metaphorical lexical items per source domain is
semi-automatic semantic tagging (Demmen et al., 2015). However, in the absence of an out-of-
the-box semantic tagger for German, we rely on a ‘traditional’ onomasiological resource
(Dornseiff, 2004). Metaphors are identified by (1) detecting seed words for target domains, (2a-
c) detecting source domain seed words in the textual neighborhood of target domain seed
words: the metaphor vehicles. Potential metaphors are examined and assigned to a typology of
mappings by inspection of KWICs (3).
Step 1. To identify target domain seed words, we compile a list of ‘objects of reading
experience’ (OREs), i.e. noun lemmas that refer to aspects of reading (literary works, such as
Buch ‘book’, Geschichte ‘story’, Roman ‘novel’ and parts thereof, such as Ende ‘ending’ or
Spannung ‘suspense’, see Table 1).
Step 2. (2a) Potential source domains are pre-identified by manual MIPVU annotation of small
samples of the data (cf. Herrmann et al., in press), and the literature on ‘reading’ metaphors
(e.g. Nuttall & Harrison, 2018). For the present paper, we focus on conceptualizations of reading
experiences as MOTION (see Herrmann & Messerli, submitted, for metaphor vehicles from the
domain FOOD INTAKE). (2b) The lexical access points to the MOTION domain are provided by
a word list extracted from Dornseiff (2004) for the semantic field Fortbewegung (8.3, see Table
2). (2c) To find potential metaphor vehicles that refer to ORE (and not to some other referent),
cooccurrences are computed between
‘motion’ lemmas and ORE, with a window of 10 lemmas around ORE (using raw frequencies,
see Table 3).
Step 3. From the resulting frequency list of potential ‘motion’-metaphor vehicles (n= 389,689) a
sub-section of the most common lemmas is examined by means of KWICs to determine
whether potential vehicles were indeed used metaphorically. In a qualitative step, we infer
usage patterns from the resulting true metaphor positives (Table 4).
The LoBo corpus (extracted from the social reading platform “Lovelybooks”) contains approx.
1.3 mill. German language reviews by 54,000 users, amounting to 439,923,000 words (Table 4),
spread over 15 genres. Each review features a rating (1–5 stars) that refers to a specific book.
The corpus is lemmatized and PoS-tagged with TreeTagger (Schmid, 1994), and encoded in
Table 4. Overview of word frequencies of ORE and source domain seed words in LoBo
A first result is a list of those lemmas from the semantic field Fortbewegung ‘motion’ that occur
frequently within a window of ten words of ORE. While it does not yet allow for conclusive
results regarding metaphor use, this list serves as an intermediary step towards identifying a
multitude of MOTION metaphors for subsequent analysis establishing a typology of mapping
The analysis of KWICs shows that certain manners of motion are particularly frequent. Notable
are the motions of walking, flying, and driving/riding, realized with the lemmas gehen ‘to go’,
fliegen ‘to fly’, and Fahrt ‘ride/drive’. Notable is variance of ‘speed’, with fast motion (Fahrt,
fliegen), and slower motion (gehen).
Another important observation is about agency within the metaphorical scenario. Readers
position themselves mainly as (a) observers who see how the plot moves along; (b) agents who
actively ‘walk’ and ‘fly’ through the story (or a book’s pages); (c) patients being put in motion by
the book; and (d) companions who travel along with an ORE (see Table 5). Findings
Our aim here is not the identification of phraseological units by significance tests against chance
distribution (Mutual Information, DICE, and log-likelihood). Rather, raw frequencies allow us to define a
window of reference for metaphor vehicles.
demonstrate the complexity of reading that cannot be restricted to passive reception or
hedonistic consumption (cf. Rebora et al., 2019).
In all, our study offers a first typology of metaphorical MOTION-mappings in digital shared
reading, as well as evidence of the productivity of MOTION as a source domain for READING in
German lay reviews (cf. Nuttall & Harrison, 2019, for English reviews). Extending this
exploratory phase into statistical analysis, we plan variance analysis with factors as reader’s
evaluation (star ratings) and book genre (e.g., middle brow vs. popular). Methodologically, we
plan to improve precision of metaphor detection, e.g. by including semantic information from
resources such as GermaNet, but also through active learning. Generally, further examination of
metaphors will allow valuable insight into underlying conceptual and value systems in reader
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