title: Guidelines for Effective Usage of Text Highlighting Techniques.
abstract: Semi-automatic text analysis involves manual inspection of text. Often, different text annotations (like part-of-speech or named entities) are indicated by using distinctive text highlighting techniques. In typesetting there exist well-known formatting conventions, such as bold typeface, italics, or background coloring, that are useful for highlighting certain parts of a given text. Also, many advanced techniques for visualization and highlighting of text exist; yet, standard typesetting is common, and the effects of standard typesetting on the perception of text are not fully understood. As such, we surveyed and tested the effectiveness of common text highlighting techniques, both individually and in combination, to discover how to maximize pop-out effects while minimizing visual interference between techniques. To validate our findings, we conducted a series of crowdsourced experiments to determine: i) a ranking of nine commonly-used text highlighting techniques; ii) the degree of visual interference between pairs of text highlighting techniques; iii) the effectiveness of techniques for visual conjunctive search. Our results show that increasing font size works best as a single highlighting technique, and that there are significant visual interferences between some pairs of highlighting techniques. We discuss the pros and cons of different combinations as a design guideline to choose text highlighting techniques for text viewers.
authors.forename: Hendrik
authors.initials: H
authors.lastname: Strobelt
authors.forename: Daniela
authors.initials: D
authors.lastname: Oelke
authors.forename: Bum Chul
authors.initials: BC
authors.lastname: Kwon
authors.forename: Tobias
authors.initials: T
authors.lastname: Schreck
authors.forename: Hanspeter
authors.initials: H
authors.lastname: Pfister
mesh_terms: D000328:Adult; D003196:Computer Graphics; D063045:Crowdsourcing; D066289:Data Curation / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D055815:Young Adult