Grounded Theory combines the data collection and analysis phases of the research, so that you continually analyze data as you collect it, and then modify your additional data collection to further your additional analysis. That would be a rare design in working with social media, so I recommend content analysis.

If you are emphasizing latent content, the most common approach to content analysis would be to develop your own codes, inductively.

hI Nejc Ašič ,

The following information may be useful to you.

https://www.researchgate.net/post/What-are-the-best-ways-to-content-analyze-social-media-streams

Article Analysis of Comments of Users of Social Networks to Assess t...

Content analysis is a research technique used to make replicable and valid inferences by interpreting and coding textual material. By systematically evaluating texts (e.g., documents, oral communication, and graphics), qualitative data can be converted into quantitative data.

How to conduct content analysis

1. Select the content you will analyze. Based on your research question, choose the texts that you will analyze. ...

2. Define the units and categories of analysis. ...

3. Develop a set of rules for coding. ...

4. Code the text according to the rules. ...

5. Analyze the results and draw conclusions.

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings and relationships of such certain words, themes, or concepts.

Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conven tional, directed, or summative. ... In conventional content analysis, coding categories are derived directly from the text data.

Content analysis is a method for summarizing any form of content by counting various aspects of the content. ... For example, an impressionistic summary of a TV program, is not content analysis. Nor is a book review: it's an evaluation. Content analysis, though it often analyses written words, is a quantitative method.

Disadvantages of Content Analysis

Content analysis suffers from several disadvantages, both theoretical and procedural. In particular, content analysis:

• can be extremely time consuming
• is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation
• is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study
• is inherently reductive, particularly when dealing with complex texts
• tends too often to simply consist of word counts
• often disregards the context that produced the text, as well as the state of things after the text is produced
• can be difficult to automate or computerize

Grounded Theory

How can you analyze a vast set qualitative data acquired through participant observation, in-depth interviews, focus groups, narratives of audio/video recordings, or secondary documents? One of these techniques for analyzing text data is grounded theory – an inductive technique of interpreting recorded data about a social phenomenon to build theories about that phenomenon. The technique was developed by Glaser and Strauss (1967) [18] in their method of constant comparative analysis of grounded theory research, and further refined by Strauss and Corbin (1990) [19] to further illustrate specific coding techniques – a process of classifying and categorizing text data segments into a set of codes (concepts), categories (constructs), and relationships. The interpretations are “grounded in” (or based on) observed empirical data, hence the name. To ensure that the theory is based solely on observed evidence, the grounded theory approach requires that researchers suspend any preexisting theoretical expectations or biases before data analysis, and let the data dictate the formulation of the theory.

Strauss and Corbin (1998) describe three coding techniques for analyzing text data: open, axial, and selective. Open coding is a process aimed at identifying concepts or key ideas that are hidden within textual data, which are potentially related to the phenomenon of interest. The researcher examines the raw textual data line by line to identify discrete events, incidents, ideas, actions, perceptions, and interactions of relevance that are coded as concepts (hence called in vivo codes ). Each concept is linked to specific portions of the text (coding unit) for later validation. Some concepts may be simple, clear, and unambiguous while others may be complex, ambiguous, and viewed differently by different participants. The coding unit may vary with the concepts being extracted. Simple concepts such as “organizational size” may include just a few words of text, while complex ones such as “organizational mission” may span several pages. Concepts can be named using the researcher’s own naming convention or standardized labels taken from the research literature. Once a basic set of concepts are identified, these concepts can then be used to code the remainder of the data, while simultaneously looking for new concepts and refining old concepts. While coding, it is important to identify the recognizable characteristics of each concept, such as its size, color, or level (e.g., high or low), so that similar concepts can be grouped together later . This coding technique is called “open” because the researcher is open to and actively seeking new concepts relevant to the phenomenon of interest.

Next, similar concepts are grouped into higher order categories . While concepts may be context-specific, categories tend to be broad and generalizable, and ultimately evolve into constructs in a grounded theory. Categories are needed to reduce the amount of concepts the researcher must work with and to build a “big picture” of the issues salient to understanding a social phenomenon. Categorization can be done is phases, by combining concepts into subcategories, and then subcategories into higher order categories. Constructs from the existing literature can be used to name these categories, particularly if the goal of the research is to extend current theories. However, caution must be taken while using existing constructs, as such constructs may bring with them commonly held beliefs and biases. For each category, its characteristics (or properties) and dimensions of each characteristic should be identified. The dimension represents a value of a characteristic along a continuum. For example, a “communication media” category may have a characteristic called “speed”, which can be dimensionalized as fast, medium, or slow . Such categorization helps differentiate between different kinds of communication media and enables researchers identify patterns in the data, such as which communication media is used for which types of tasks.

The second phase of grounded theory is axial coding , where the categories and subcategories are assembled into causal relationships or hypotheses that can tentatively explain the phenomenon of interest. Although distinct from open coding, axial coding can be performed simultaneously with open coding. The relationships between categories may be clearly evident in the data or may be more subtle and implicit. In the latter instance, researchers may use a coding scheme (often called a “coding paradigm”, but different from the paradigms discussed in Chapter 3) to understand which categories represent conditions (the circumstances in which the phenomenon is embedded), actions/interactions (the responses of individuals to events under these conditions), and consequences (the outcomes of actions/ interactions). As conditions, actions/interactions, and consequences are identified, theoretical propositions start to emerge, and researchers can start explaining why a phenomenon occurs, under what conditions, and with what consequences.

The third and final phase of grounded theory is selective coding , which involves identifying a central category or a core variable and systematically and logically relating this central category to other categories. The central category can evolve from existing categories or can be a higher order category that subsumes previously coded categories. New data is selectively sampled to validate the central category and its relationships to other categories (i.e., the tentative theory). Selective coding limits the range of analysis, and makes it move fast. At the same time, the coder must watch out for other categories that may emerge from the new data that may be related to the phenomenon of interest (open coding), which may lead to further refinement of the initial theory. Hence, open, axial, and selective coding may proceed simultaneously. Coding of new data and theory refinement continues until theoretical saturation is reached, i.e., when additional data does not yield any marginal change in the core categories or the relationships.

The “constant comparison” process implies continuous rearrangement, aggregation, and refinement of categories, relationships, and interpretations based on increasing depth of understanding, and an iterative interplay of four stages of activities: (1) comparing incidents/texts assigned to each category (to validate the category), (2) integrating categories and their properties, (3) delimiting the theory (focusing on the core concepts and ignoring less relevant concepts), and (4) writing theory (using techniques like memoing, storylining, and diagramming that are discussed in the next chapter). Having a central category does not necessarily mean that all other categories can be integrated nicely around it. In order to identify key categories that are conditions, action/interactions, and consequences of the core category, Strauss and Corbin (1990) recommend several integration techniques, such as storylining, memoing, or concept mapping. In storylining , categories and relationships are used to explicate and/or refine a story of the observed phenomenon. Memos are theorized write-ups of ideas about substantive concepts and their theoretically coded relationships as they evolve during ground theory analysis, and are important tools to keep track of and refine ideas that develop during the analysis. Memoing is the process of using these memos to discover patterns and relationships between categories using two-by-two tables, diagrams, or figures, or other illustrative displays. Concept mapping is a graphical representation of concepts and relationships between those concepts (e.g., using boxes and arrows). The major concepts are typically laid out on one or more sheets of paper, blackboards, or using graphical software programs, linked to each other using arrows, and readjusted to best fit the observed data.

After a grounded theory is generated, it must be refined for internal consistency and logic. Researchers must ensure that the central construct has the stated characteristics and dimensions, and if not, the data analysis may be repeated. Researcher must then ensure that the characteristics and dimensions of all categories show variation. For example, if behavior frequency is one such category, then the data must provide evidence of both frequent performers and infrequent performers of the focal behavior. Finally, the theory must be validated by comparing it with raw data. If the theory contradicts with observed evidence, the coding process may be repeated to reconcile such contradictions or unexplained variations.

Content Analysis

Content analysis is the systematic analysis of the content of a text (e.g., who says what, to whom, why, and to what extent and with what effect) in a quantitative or qualitative manner. Content analysis typically conducted as follows. First, when there are many texts to analyze (e.g., newspaper stories, financial reports, blog postings, online reviews, etc.), the researcher begins by sampling a selected set of texts from the population of texts for analysis. This process is not random, but instead, texts that have more pertinent content should be chosen selectively. Second, the researcher identifies and applies rules to divide each text into segments or “chunks” that can be treated as separate units of analysis. This process is called unitizing . For example, a ssumptions, effects, enablers, and barriers in texts may constitute such units. Third, the researcher constructs and applies one or more concepts to each unitized text segment in a process called coding . For coding purposes, a coding scheme is used based on the themes the researcher is searching for or uncovers as she classifies the text. Finally, the coded data is analyzed, often both quantitatively and qualitatively, to determine which themes occur most frequently, in what contexts, and how they are related to each other.

A simple type of content analysis is sentiment analysis – a technique used to capture people’s opinion or attitude toward an object, person, or phenomenon. Reading online messages about a political candidate posted on an online forum and classifying each message as positive, negative, or neutral is an example of such an analysis. In this case, each message represents one unit of analysis. This analysis will help identify whether the sample as a whole is positively or negatively disposed or neutral towards that candidate. Examining the content of online reviews in a similar manner is another example. Though this analysis can be done manually, for very large data sets (millions of text records), natural language processing and text analytics based software programs are available to automate the coding process, and maintain a record of how people sentiments fluctuate with time.

A frequent criticism of content analysis is that it lacks a set of systematic procedures that would allow the analysis to be replicated by other researchers. Schilling (2006) [20] addressed this criticism by organizing different content analytic procedures into a spiral model. This model consists of five levels or phases in interpreting text: (1) convert recorded tapes into raw text data or transcripts for content analysis, (2) convert raw data into condensed protocols, (3) convert condensed protocols into a preliminary category system, (4) use the preliminary category system to generate coded protocols, and (5) analyze coded protocols to generate interpretations about the phenomenon of interest.

Content analysis has several limitations. First, the coding process is restricted to the information available in text form. For instance, if a researcher is interested in studying people’s views on capital punishment, but no such archive of text documents is available, then the analysis cannot be done. Second, sampling must be done carefully to avoid sampling bias. For instance, if your population is the published research literature on a given topic, then you have systematically omitted unpublished research or the most recent work that is yet to be published.

Hermeneutic Analysis

Hermeneutic analysis is a special type of content analysis where the researcher tries to “interpret” the subjective meaning of a given text within its socio-historic context. Unlike grounded theory or content analysis, which ignores the context and meaning of text documents during the coding process, hermeneutic analysis is a truly interpretive technique for analyzing qualitative data. This method assumes that written texts narrate an author’s experience within a socio-historic context, and should be interpreted as such within that context. Therefore, the researcher continually iterates between singular interpretation of the text (the part) and a holistic understanding of the context (the whole) to develop a fuller understanding of the phenomenon in its situated context, which German philosopher Martin Heidegger called the hermeneutic circle. The word hermeneutic (singular) refers to one particular method or strand of interpretation.

More generally, hermeneutics is the study of interpretation and the theory and practice of interpretation. Derived from religious studies and linguistics, traditional hermeneutics, such as biblical hermeneutics , refers to the interpretation of written texts, especially in the areas of literature, religion and law (such as the Bible). In the 20th century, Heidegger suggested that a more direct, non-mediated, and authentic way of understanding social reality is to experience it, rather than simply observe it, and proposed philosophical hermeneutics , where the focus shifted from interpretation to existential understanding. Heidegger argued that texts are the means by which readers can not only read about an author’s experience, but also relive the author’s experiences. Contemporary or modern hermeneutics, developed by Heidegger’s students such as Hans-Georg Gadamer, further examined the limits of written texts for communicating social experiences, and went on to propose a framework of the interpretive process, encompassing all forms of communication, including written, verbal, and non-verbal, and exploring issues that restrict the communicative ability of written texts, such as presuppositions, language structures (e.g., grammar, syntax, etc.), and semiotics (the study of written signs such as symbolism, metaphor, analogy, and sarcasm). The term hermeneutics is sometimes used interchangeably and inaccurately with exegesis , which refers to the interpretation or critical explanation of written text only and especially religious texts.

Conclusions

Finally, standard software programs, such as ATLAS.ti.5, NVivo, and QDA Miner, can be used to automate coding processes in qualitative research methods. These programs can quickly and efficiently organize, search, sort, and process large volumes of text data using user-defined rules. To guide such automated analysis, a coding schema should be created, specifying the keywords or codes to search for in the text, based on an initial manual examination of sample text data. The schema can be organized in a hierarchical manner to organize codes into higher-order codes or constructs. The coding schema should be validated using a different sample of texts for accuracy and adequacy. However, if the coding schema is biased or incorrect, the resulting analysis of the entire population of text may be flawed and non-interpretable. However, software programs cannot decipher the meaning behind the certain words or phrases or the context within which these words or phrases are used (such as those in sarcasms or metaphors), which may lead to significant misinterpretation in large scale qualitative analysis.

[17] Miles M. B., Huberman A. M. (1984). Qualitative Data Analysis: A Sourcebook of New Methods . Newbury Park, CA: Sage Publications.

[18] Glaser, B. and Strauss, A. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research , Chicago: Aldine.

[19] Strauss, A. and Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques , Beverly Hills, CA: Sage Publications.

[20] Schilling, J. (2006). “On the Pragmatics of Qualitative Assessment: Designing the Process for Content Analysis,” European Journal of Psychological Assessment (22:1), 28-37.

best wishes

Hi Nejc Ašič,

Considering your data is more likely to be collected from social media and falls into the category of unobtrusive data I would suggest content analysis to be used. As content analysis has more options to select from while choosing inductive or deductive approaches and later if one wants to quantify the data, the content analysis can use both qualitative or quantitative methods to analyse your data.

In contrast, the grounded theory approach is bottom up approach or inductive approach, where you as researcher are trying to generate theory from the data and using grounded theory approach you are going to try and ignore any theoretical preconception. Grounded theory is best used when you are not following existing theories to develop your own theory. Whereas, in content analysis you are open with more options to choose inductive or deductive approaches. Hope this answers your query.

Grounded Theory combines the data collection and analysis phases of the research, so that you continually analyze data as you collect it, and then modify your additional data collection to further your additional analysis. That would be a rare design in working with social media, so I recommend content analysis.

If you are emphasizing latent content, the most common approach to content analysis would be to develop your own codes, inductively.

Generally Grounded Theory is used when we don't have a relavant literature. From interviews we generate concepts, and from concepts we come up with a theory.

Very good luck!

Given your topic you could consider applying a Content Analysis within a Discourse analytical approach. This brief pdf can help you find further references.

https://core.ac.uk/download/pdf/144874836.pdf

Thank you all for your answers. They helped me a lot. I will definitely not use The Grounded Theory.

I have just one question - would be more correctly to use Thematic Analyis instead of Content Analysis? According to my research data (comments on social media).

I can not really understand the real difference between these two approaches (Content a. vs. Thematic a.) - only that CA is more quantitative and TA is more qualitative. What is useful at Thematic Analysis is that I can use hybrid approach that combines inductive and deductive coding (Fereday and Muir-Cochrane, 2006).

Thank you so much for answer.

Best regards

There are basically two kinds of content analysis. One is more based on deductive coding systems and emphasizes manifest content, which leads to the counting of codes. The other is based on inductive coding systems and emphasizes latent content, which leads to more interpretive analysis. In my opinion, this second version of content analysis is pretty much indistinguishable from thematic analysis.

In a (inductive) Qualitative Content Analysis code 'frequencies' may still apply and be used to develop the topics and ultimately themes, whereas TA is purely qualitative and interpretative.

Content analysis.

Grounded theory usually used if you have big number of questions and related each other.

Content analysis. Develop themes as they emerge from the information you gather.

Content analysis, Doreen.

Sugiero técnicas cualitativas: análisis de contenido. Además, sería bueno analizar la pertinencia de uso del análisis del discurso

I'm doing grounded theory with my PhD research, almost done. I write a paper for new GT researchers (under review). Honestly, it will be a big challenge if you want to use GT in a master thesis. content analysis may be a good option, and you will find more flexible method to collect data.

Content Analysis would be useful if you are looking for a scaffold of method,as opposed to grounded theory.However,if the specificity of the research is focused on social media, thematic analysis may be the most desirable.

Necj Depend of your question. Is you have a research question referring to what journalist talk about or what are the news that they inform to the mass, you have to use content analisys. Grounded theory answer other kind of research question.

There is quantitative "content analysis"; if you want to know about how many times some topics is mentioned at one or more journals? which are the words uses to inform those topics? the botton line or the essential is to be clear what you are looking for. Good luck!!

Content analysis suits your study. Good luck