Content analysis

What is content analysis?

Content analysis is a systematic method of analysing textual, visual or audio content to identify patters, themes and meanings

It is commonly used to study the underlying messages in various forms of communication, such as social media posts, news articles, or interviews


Different types of content analysis

Content Analysis (CA) can be both quantitative and qualitative. While it originally began with quantitative methods, it cannot be limited to these alone. Kracauer, in the early 1950s, emphasized that Quantitative content analysis is concerned with manifest, literal meaning- whereas Qualitative content analysis focuses on latent meaning—meanings that are not immediately apparent.


  • Quantitative:

    • To explore the impact of crime on older adults, one could look at
      news stories about them and count how often words like crime, scams, abuse, and victim appear in connection to them

  • Qualitative:

    • To explore how victimisation among older adults is discussed in the news, one could look for the word “victim" in news stories about them and see what other words show up near it, like vulnerable, easy target, stupid. This would helpful in understanding how the media portrays older adults in the context of victimisation


Why qualitative content analysis

Kracauer (1952) argues:

  • Meaning is often complex holistic and context- dependent

  • Meaning does not always manifest itself in a clear first sight

  • Some aspects of the meaning may appear only once in the data but this does not automatically mean that are less important


Purpose of qualitative content analysis

Content analysis can be used for numerous research goals including:

  • discovering correlations and patterns in how particular concepts or ideas are communicated;

  • identifying bias in communication (e.g. political, racist, heteronormative, etc);

  • knowing the intentions of an institution, group, or individual;

  • investigating change in public opinion


Content analysis in criminology

  • Analysing Social Media & Online Forums:

    • Monitoring discussions on social platforms to detect trends in criminal behaviour or radicalisation.

    • Examining posts for hate speech, recruitment tactics, or other signs of illicit activities

  • Studying Media Representation:

    • Assessing how crime, policing, or marginalised groups are portrayed in news outlets.

    • Identifying bias, stereotypes, or sensationalism in media reporting of criminal cases


Potential data sources

Offline:

  • Historical documents and archives

  • Personal diaries

  • Movies

  • Interviews / focus groups


Online:

  • Website

  • Blogs and vlogs

  • Chatrooms, forums and online communities

  • Social media


Using archival material in content analysis

Archival content analysis involves studying historical records, documents, case files, or other preserved materials to understand past criminal behaviours, social reactions, or policy developments.

Examples in Criminological Research:

  • Court Records & Police Reports: Analysing past case files
    to study patterns in sentencing, racial disparities, or the
    impact of legal reforms.

  • Historical Newspapers: Examining how historical crimes
    were reported to understand shifts in public perception and
    media influence.

  • Government Documents & Legislation: Investigating how
    laws related to crime and punishment have evolved over
    time.

Using data from the past (historical criminology)

  • Proceedings from the Old Bailey

  • British newspaper archive

  • Parliamentary papers

  • Criminal register

  • British newspaper 1600-1900


Steps

  1. Deciding on your research question

  2. Selecting your material

  3. Building a coding frame

  4. Dividing your material into units of coding

  5. Trying out your coding frame

  6. Evaluating and modifying your coding frame

  7. Main analysis


Deciding on your research question

Before conducting QCA, you need to:

  • Specify the angle of your analysis: QCA helps you describe your material in specific respects, so you must clearly define the focus of your analysis. This approach is especially useful when working with rich data, as it allows you to concentrate on one particular aspect.

  • Be clear about the goals of your analysis: Are you aiming solely to describe the representation of a phenomenon, or do you want to use your data to draw broader implications about the topic?


Example:

  • Remember that with CA, the research question can explore currently inaccessible and/or sensitive phenomena

  • Research Question: Media Representations of Child Sex Abuse and Offenders before and after the enforcement of Sarah’s Law


Selecting the material

Think about what you want to analysis:

  • Including/excluding criteria

  • Media outlets?

  • Time frame?

  • Which key terms?


Selecting your coding frame

What is the focus of your analysis? Which lenses are you applying to study the phenomenon?

  • The aspects you are interested in will define the categories (dimensions) of your coding frame (e.g. pedophilia representation)

  • Each category will have a few options (sub-categories)

  • Category: offenders’ representation, causes of paedophilia, etc.

  • Sub-Categories of offenders’ representation: gender, age, kinship

  • Sub-Categories of causes of paedophilia: (e.g. biological psychological, social)

Dividing your material into units of coding

  • Segmenting your material

  • First highlight material that you think is relevant to answer your research question. Then, segment your material into units which could be assigned to a category


Case study: De Benedicts et al. (2019)

  • Study Aim: to analyse the coverage of #MeToo as a feminist campaign focusing on sexual violence

  • Material Selection: Search terms (#MeToo; MeToo); Time Frame (11 October 2017- 31 March 2018); Newspaper articles online across 9 major UK newspapers; random sampling

  • Coding frame: Type of Publication, overall tone, focus of the article, main sector/industry/work context

  • Example of Category: overall tone; Sub-category: positive, negative, mixed/balanced, unclear

  • Positive: commendation/appraisal/valuing/appreciation/recognition

  • Negative: demonstrated or included substantial criticism/derision/cynicism/dismissal

  • Mixed/balanced: includes some positive and negative commentary

Why choosing CA?

  • Unobtrusive method

  • Data is not reactive. The techniques of data collection do not impact data. E.g. desirability bias

  • It is transparent and consistent. The coding scheme and the sampling procedures can be clearly set out so that replications and follow-up studies can be conducted

  • It can be longitudinal. It allows the researcher to track changes in frequency over various different periods of time

  • It is flexible. This method can be applied to a wide variety of unstructured sources.

  • It allows researchers to gain access to hard-to-reach groups. Content analysis provides a way to generate information about social groups to which it is difficult to gain access. E.g. It is unlikely to gain access to interview the prime minister or preside nt, but researchers could analyse their speeches (Bligh et al. 2004) or their tweets in the build-up to an election (Gunn and Napier 2016).

  • Through segmentation, it allows researchers to analyse materials in their wholeness trying to minimise biases (e.g. overlooking some aspects because focusing only on what they are interested in)


Limitations

  • QCA It depends on the quality of the documents/data. Are the data authentic? Representative? (Krippendorff, 2018)

  • The difficulty of answering ‘why?’ questions. Qualitative Content Analysis can describe data and its meaning. However, explanations as to why are often based on speculations


Ethical concerns in online criminological research

The ethics approval process is mostly straightforward as the data is widely available in public arenas.

Despite apparent little ethical obligation, due to the sensitive nature of some topics, you should consider to:

  • Do not include any data from accounts that suggests the underage of the users

  • Minimise the identifiability of any individuals named in news stories in subsequent publications of the findings (Amundsen, 2022)

  • Blurred Boundaries: Social media can appear public but is often perceived as private.

  • Data Protection: Secure storage and compliance with laws like GDPR.

  • Risk of Harm: Online studies can expose participants to legal or social risks.

  • Re-traumatization: Handling sensitive content with care.

  • Researcher Responsibility

  • Avoiding Exploitation: Ethical use of online content.

  • Ongoing Ethical Reflexivity: Continuously reassessing ethical considerations.

  • Automated Tools: Use AI and web scraping cautiously to avoid privacy breaches