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  "whatIsVerbatim": "Only 'quote' fields, figure captions and 'abstract' are the authors' own words. Research questions follow the paper's wording where it states them. Takeaway, summaries, answers, definitions and method are the site's plain-language paraphrase.",
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 "name": "Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis",
 "author": [
  {
   "@type": "Person",
   "name": "Daniel Angus"
  },
  {
   "@type": "Person",
   "@id": "https://seanrintel.com/#sean-rintel",
   "name": "Sean Rintel",
   "url": "https://seanrintel.com/",
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    "https://www.microsoft.com/en-us/research/people/serintel/",
    "https://www.linkedin.com/in/seanrintel"
   ]
  },
  {
   "@type": "Person",
   "name": "Janet Wiles"
  }
 ],
 "datePublished": "2013-04",
 "doi": "10.1080/13645579.2013.774186",
 "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html",
 "citation": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186",
 "bibtex": "@article{angus2013making,\nauthor = {Angus, Daniel and Rintel, Sean and Wiles, Janet},\ntitle = {Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis},\nyear = {2013},\nmonth = {April},\nurl = {https://doi.org/10.1080/13645579.2013.774186},\npages = {261-267},\njournal = {International Journal of Social Research Methodology},\nvolume = {16},\nnumber = {3},\nnote = {Angus, D., Rintel, S. & Wiles, J. (2013). Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis, International Journal of Social Research Methodology (Special Issue: Computational Social Science: Research Strategies, Design and Methods) 16 (3), 261-267},\ndoi = {10.1080/13645579.2013.774186},\n}",
 "abstract": "This article reports on Leximancer and Discursis, two visual text analytic software tools developed at the University of Queensland. Both analyse spatial and temporal relationships in text data, but in complementary ways: Leximancer focuses on thematic analysis, while Discursis focuses on sequential analysis. Our report explains how they work, how to work with them and how visual concepts are relevant to all stages of their use in analytic decision-making.",
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 "fullTextAvailable": true,
 "takeaway": "Visual-first text analytics turns visualisation from end-stage output into the start of analysis. Software maps a large text's themes and how topics recur over time, and researchers interpret and adjust those relationships rather than hand-coding them. This can reveal subtle patterns and make concept lists reproducible.",
 "whyItMatters": {
  "stateOfTheArt": {
   "summary": "Established qualitative analysis software helps researchers code and organise text, but the workflow stays textual and visuals are mostly used to display the researcher's themes at the end.",
   "quote": "researchers do the work of metaphorically handling the text to create themes for discussion",
   "location": "§1, p. 3"
  },
  "contribution": {
   "summary": "A practical introduction to two tools from the University of Queensland, one for themes and one for conversation over time. They are demonstrated on a single TV interview and set against the hand-coding workflow of established qualitative analysis software.",
   "quote": "Leximancer and Discursis highlight a visual approach to analysis which changes the way text is used as evidence and forms the basis for decision-making",
   "location": "§1, p. 3"
  },
  "whoCares": {
   "summary": "Social researchers and discourse analysts working with large text data-sets, across very different fields.",
   "quote": "Such motifs would allow researchers from very disparate fields to find patterns of relevance, both expected and unexpected.",
   "location": "§4, p. 6"
  }
 },
 "researchQuestions": [
  "How do Leximancer and Discursis work, how should researchers work with them, and how are visual concepts relevant to all stages of their use in analytic decision-making?"
 ],
 "concepts": [
  {
   "term": "Visual text analytics",
   "definition": "The merging of text analysis and information visualisation.",
   "quote": "techniques that visually model text data for interpretation by a researcher",
   "location": "§1, p. 2"
  },
  {
   "term": "CAQDAS",
   "definition": "Established tools such as NVivo, Atlas.ti and MAXQDA:",
   "quote": "since the 1990s computer-aided qualitative discourse analysis software (CAQDAS) applications have provided the means for semi-automated analysis of conversation, interview, mass media and new media data",
   "location": "§1, p. 2"
  },
  {
   "term": "Concept map (Leximancer)",
   "definition": "An automatically generated, interactive map of the main concepts and themes in a text. How it is built:",
   "quote": "Leximancer uses word occurrence and co-occurrence counts to extract major thematic and conceptual content directly from an input text.",
   "location": "§2, p. 3"
  },
  {
   "term": "Discursis plot",
   "definition": "Discursis's visualisation, for text with a temporal structure such as a conversation:",
   "quote": "generates an interactive visual representation of the input text by linking similar temporal units",
   "location": "§3, p. 4"
  }
 ],
 "method": {
  "design": "Methods report introducing two visual text analytic software tools developed at the University of Queensland, with a worked example.",
  "setting": "Example analyses of a transcript of the ABC Enough Rope Programme (aired 4 October 2004), a conversation between host Andrew Denton and Prof. Peter Singer.",
  "analysis": "Leximancer for thematic analysis (concept maps). Discursis for sequential analysis (conceptual recurrence across turns), both based on word frequency statistics."
 },
 "findings": [
  {
   "headline": "Two tools, two jobs.",
   "summary": "Leximancer and Discursis do complementary jobs. Leximancer finds the main topics in a text, how they relate and who uses them, while Discursis shows how topics are used over time, where they change and where they recur.",
   "quote": "Leximancer focuses on thematic analysis, while Discursis focuses on sequential analysis",
   "location": "Abstract, p. 2",
   "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-abstract-p-2",
   "cite": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186 (Abstract, p. 2)"
  },
  {
   "headline": "From building themes to interpreting them.",
   "summary": "Because both tools generate the relationships in a text automatically, the researcher's task shifts from defining relationships, as in hand-coding, to making sense of the ones displayed, and then splitting, combining and refining them.",
   "quote": "Critically, this is a process of adjusting the software's automation rather than a process of manual arrangement using software.",
   "location": "§1, p. 3",
   "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s1-p-3",
   "cite": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186 (§1, p. 3)"
  },
  {
   "headline": "Automatic concept lists are reproducible.",
   "summary": "Leximancer builds its own concept list from the text, instead of asking analysts to design lists of codes, and subtle or unusual relationships may be more likely to emerge. The comparison with manual coding:",
   "quote": "the list is statistically reliable and reproducible, being generated from the input text itself, whereas manual lists require checks for coding reliability and validity",
   "location": "§2, p. 3",
   "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s2-p-3",
   "cite": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186 (§2, p. 3)"
  },
  {
   "headline": "Patterns at several time scales at once.",
   "summary": "Discursis shows topic use at several time scales simultaneously, from turn by turn to the whole conversation. Earlier studies by Angus and colleagues of talk shows, medical consultations, cockpit recordings and phone calls used it to find topic convergence between speakers and, for example:",
   "quote": "whole conversation call-back behaviours (where early mentioned topics were revisited much later in the conversation)",
   "location": "§3, p. 5",
   "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s3-p-5",
   "cite": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186 (§3, p. 5)"
  },
  {
   "headline": "The visual becomes part of reasoning.",
   "summary": "In social research, visuals have mostly served as end-stage output for readers. Visual-first tools bring them into reasoning and decision-making about the data. The authors' assessment:",
   "quote": "tools such as Leximancer and Discursis are powerful techniques for developing evidence-based global analyses that would otherwise tax the cognitive abilities of an analyst",
   "location": "§4, p. 6",
   "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s4-p-6",
   "cite": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186 (§4, p. 6)"
  }
 ],
 "questions": [
  {
   "question": "When does a visual tool help more than reading the text yourself?",
   "answer": "When the data are too large to hold in mind. Visual text analytics give a global view of a whole data-set that would otherwise tax an analyst. They then let the analyst drill down to the original passages, so overview and close reading support each other.",
   "source": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186",
   "basedOn": [
    {
     "location": "§1, p. 3",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s1-p-3"
    },
    {
     "location": "§4, p. 6",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s4-p-6"
    }
   ]
  },
  {
   "question": "Can visual text analysis test a theory, or only explore data?",
   "answer": "Both. Earlier studies used Discursis (a tool that maps which turns of a conversation share topics) on talk shows, medical consultations, cockpit recordings and phone calls. Its visuals helped analysts confirm theory-driven hypotheses about how speakers reuse topics, and discover unexpected patterns of interaction.",
   "source": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186",
   "basedOn": [
    {
     "location": "§3, p. 5",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s3-p-5"
    }
   ]
  },
  {
   "question": "Is automated coding more trustworthy than hand-coding?",
   "answer": "It is more reproducible, because a concept list generated statistically from the text itself does not depend on who codes it. Hand-made code lists, by contrast, need reliability and validity checks. Automated coding may also surface unusual relationships. The researcher still interprets and adjusts what the software produces.",
   "source": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186",
   "basedOn": [
    {
     "location": "§1, p. 3",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s1-p-3"
    },
    {
     "location": "§2, p. 3",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s2-p-3"
    }
   ]
  },
  {
   "question": "Can software show how a conversation develops over time?",
   "answer": "Yes. One such tool, Discursis, lays out every turn of a conversation and shades where turns share concepts. An analyst can then see at a glance who speaks when, where speakers converge on topics, and where early topics are revisited much later.",
   "source": "Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186",
   "basedOn": [
    {
     "location": "Abstract, p. 2",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-abstract-p-2"
    },
    {
     "location": "§3, p. 5",
     "url": "https://seanrintel.com/publications/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis.html#at-s3-p-5"
    }
   ]
  }
 ],
 "cites": [],
 "quantitative": [],
 "implications": [
  {
   "summary": "Visual text analytics should be extended to more theoretical frameworks, as a new operationalisation of evidentiary procedures."
  },
  {
   "summary": "A library or taxonomy of visual motifs could help researchers make visual sense of their data and re-evaluate new data rapidly."
  },
  {
   "summary": "The tools support rather than replace human analysts.",
   "quote": "techniques such as Leximancer and Discursis are not designed to replace the role of the human analyst in the social sciences",
   "location": "§4, p. 6"
  }
 ],
 "limitations": [
  {
   "summary": "Visual-first social research methodology is still in its infancy, and the workflows it needs are an ongoing research agenda.",
   "quote": "Social research methodology that uses visual-first analytic methods is still in its infancy",
   "location": "§4, p. 6"
  },
  {
   "summary": "As with many new tools, they introduce new requirements into the research workflow."
  }
 ],
 "figures": [
  {
   "src": "https://seanrintel.com/publications/figures/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis/fig-1.webp",
   "caption": "Figure 1. Example Leximancer plot.",
   "alt": "Leximancer concept map of a TV interview: concept nodes such as life, child, people, animals and Andrew grouped into large shaded theme circles and linked by lines, with a thematic summary panel alongside.",
   "location": "Fig. 1, p. 4",
   "credit": "Figure 1 from Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186"
  },
  {
   "src": "https://seanrintel.com/publications/figures/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis/fig-2.webp",
   "caption": "Figure 2. Example Discursis plot - full overview.",
   "alt": "Discursis recurrence plot of the whole interview: utterance blocks run down the diagonal, labelled with topics such as ideas, people, animals, baby and child, and off-diagonal shading marks turns that share concepts.",
   "location": "Fig. 2, p. 5",
   "credit": "Figure 2 from Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186"
  },
  {
   "src": "https://seanrintel.com/publications/figures/making-sense-of-big-text-a-visual-first-approach-for-analysing-text-data-using-leximancer-and-discursis/fig-3.webp",
   "caption": "Figure 3. Example Discursis plot - zoomed.",
   "alt": "Zoomed Discursis plot with the interview turns beside their blocks. Annotations show shading where turns share the concepts 'father' and 'rash', and where 'magazine' and 'book' are conceptually related.",
   "location": "Fig. 3, p. 5",
   "credit": "Figure 3 from Daniel Angus, Sean Rintel, and Janet Wiles. 2013. Making sense of big text: A visual-first approach for analysing text data using Leximancer and Discursis. International Journal of Social Research Methodology 16 (3), 261-267. https://doi.org/10.1080/13645579.2013.774186"
  }
 ],
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 "verified": "2026-10-25",
 "studyFigure": {
  "about": "The opening figure on this paper's page, made for the site with AI assistance from this verified brief. It is not the paper's own figure. Evidence entries index this file's quantitative, findings and limitations arrays.",
  "study": {
   "kind": "single",
   "heading": "The paper",
   "summary": "A methods report introducing two visual text analysis tools from the University of Queensland, Leximancer and Discursis. It demonstrates both on the transcript of a 2004 Australian TV interview.",
   "steps": [
    {
     "label": "Two software tools",
     "detail": "Leximancer analyses themes and Discursis analyses how topics recur over time, both from word frequency statistics."
    },
    {
     "label": "A worked example",
     "detail": "Both tools are applied to a TV conversation between host Andrew Denton and philosopher Peter Singer."
    },
    {
     "label": "Comparison with hand-coding",
     "detail": "The visual workflow is set against the hand-coding workflow of established qualitative analysis software."
    }
   ]
  },
  "tool": {
   "heading": "The method",
   "steps": [
    {
     "title": "Load the text",
     "text": "The researcher gives the software a large text, such as a transcript."
    },
    {
     "title": "Map the themes",
     "text": "Leximancer counts which words occur together and builds an interactive map of the text's main concepts and themes.",
     "part": true
    },
    {
     "title": "Plot topics over time",
     "text": "Discursis links similar turns in a conversation, showing where topics change and recur.",
     "part": true
    },
    {
     "title": "Interpret and adjust",
     "text": "The researcher makes sense of the relationships shown, then splits, combines and refines them."
    }
   ],
   "note": "The software generates the relationships, and the researcher adjusts the automation instead of arranging codes by hand."
  },
  "findings": {
   "heading": "What the paper argues",
   "legend": null,
   "stages": [
    {
     "title": "What each tool does",
     "sub": "themes and sequence",
     "items": [
      {
       "dir": "none",
       "text": "The paper shows that Leximancer and Discursis do complementary jobs.",
       "detail": "Leximancer finds a text's main topics, how they relate and who uses them. Discursis shows how topics are used over time, where they change and recur.",
       "evidence": [
        "findings[0]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper describes Discursis showing topic use at several time scales at once.",
       "detail": "Earlier studies by Angus and colleagues used it on talk shows, medical consultations, cockpit recordings and phone calls, finding topics revisited much later in a conversation.",
       "evidence": [
        "findings[3]"
       ]
      }
     ]
    },
    {
     "title": "How analysis changes",
     "sub": "the researcher's role in visual-first analysis",
     "items": [
      {
       "dir": "none",
       "text": "The paper argues the researcher's task shifts from building relationships to interpreting them.",
       "detail": "Both tools generate a text's relationships automatically, so the researcher refines what is displayed instead of defining codes by hand.",
       "evidence": [
        "findings[1]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper argues Leximancer's automatic concept lists are reproducible.",
       "detail": "The list is generated from the text itself, while manual code lists need reliability checks. Subtle or unusual relationships may be more likely to emerge.",
       "evidence": [
        "findings[2]"
       ]
      },
      {
       "dir": "none",
       "text": "The paper argues visual-first tools bring visuals into reasoning about data.",
       "detail": "In social research, visuals have mostly been end-stage output for readers. The authors note that visual-first research methods are still in their infancy.",
       "evidence": [
        "findings[4]",
        "limitations[0]"
       ]
      }
     ]
    }
   ]
  },
  "keyTerm": "visual-first approach",
  "takeaway": "A visual-first approach can open text analysis as well as present its results. When software maps a large text's themes over time, researchers can interpret and refine patterns hand-coding might miss."
 }
}