TOOLS FOR PUBLIC SOCIAL SCIENCE IN THE DIGITAL AGE: Interpretative Strategies for Content Analysis in the Public GoodABSTRACT
Digital platforms have grown into powerful forms of messaging and dissemination of discourse within and across important economies including the U.S. and Brazil. While big data approaches may give us the broad strokes presented with a host of quantitative measures, these strategies are not ideally suited researchers contributing to public-facing social sciences in which narrative, storytelling, and naturally occurring discourse are prominently featured. This research begins to fill this gap by offering content analysis strategies for such interpretively oriented qualitative researchers. In providing replicable methodological tools to analyze data interpretively, this research provides a toolkit for qualitative researchers engage in Public Social Science in an age of contentious politics.
Keywords
Content Analysis; Interpretive Methods; Qualitative Methods; Public Sociology
RESUMO
As plataformas digitais tornaram-se formas poderosas de comunicação e divulgação de discursos dentro e entre economias importantes, incluindo os EUA e o Brasil. Embora as abordagens de big data possam nos fornecer uma visão geral com uma série de medidas quantitativas, essas estratégias não são ideais para pesquisadores que contribuem para as ciências sociais voltadas para o público, nas quais a narrativa, o relato de histórias e o discurso natural têm destaque. Esta pesquisa começa a preencher essa lacuna ao oferecer estratégias de análise de conteúdo para pesquisadores que usam métodos qualitativos orientados para a interpretação. Ao fornecer ferramentas metodológicas replicáveis para analisar dados de forma interpretativa, esta pesquisa oferece um conjunto de ferramentas para pesquisadores que usam métodos qualitativos e se dedicam ao estudo das ciências sociais públicas em uma era de política controversa.
Palavras-chave
Análise de conteúdo; métodos interpretativos; métodos qualitativos; sociologia pública
RESUMEN
Las plataformas digitales se han convertido en poderosas formas de comunicación y difusión del discurso dentro y entre economías importantes, como Estados Unidos y Brasil. Si bien los enfoques basados en macrodatos pueden proporcionarnos una visión general con una serie de medidas cuantitativas, estas estrategias no son las más adecuadas para los investigadores que contribuyen a las ciencias sociales orientadas al público, en las que destacan la narrativa, la narración de historias y el discurso natural. Esta investigación comienza a llenar este vacío al ofrecer estrategias de análisis de contenido para los investigadores cualitativos orientados a la interpretación. Al proporcionar herramientas metodológicas replicables para analizar los datos de forma interpretativa, esta investigación ofrece un conjunto de herramientas para los investigadores cualitativos que se dedican a las ciencias sociales públicas en una época de controversias políticas.
Palabras-clave
Análisis de contenido; métodos interpretativos; métodos cualitativos; sociología pública
The Rise of Populism and Digital Spaces
In an age of ideological hostilities fueled by digital content within and across nations in South America and North America, digital platforms have grown into powerful forms of messaging. Since the turn of the millennium, we have lived in an age of prominent populist leaders from Luiz Inácio Lula da Silva’s first victory in 2003 to populists on the right and left gaining ascendancy including Hugo Chavez, Jair Bolsonaro, Javier Milei, Claudia Sheinbaum, and Donald Trump.
Indeed, we live in such an age of contentious politics that both Lula and Trump have both been elected for non-consecutive terms and engaged in epic battles with courts in Brazil and the U.S.—that is despite coming from opposite ends of the populist political spectrum. Yet despite their growth and importance within and across important nations including the U.S. and Brazil, we have yet to marshal the tools to understand the power and sway of these populist presidents, other political movements, and indeed our political-cultural system writ large.
To grapple with these issues, social scientists have gravitated towards “big data,” massive amounts of digital data that require specialized skills, computing programs etc. Big data is useful to provide a bird’s eye view for specialists who are carefully trained to understand advanced regression models, complex visualizations, and so forth. As such, big data approaches are inaccessible to the modal citizen who lacks the advanced training that is the preserve of specialists.
All of these are worthy enterprises but they do not hold appeal to the general public that lacks the training to understand them. Instead the general public is presented with soundbites indicating the general direction of political opinion. Examples include the perennial favorite question in the U.S. “Is the U.S. going in the right direction?” or “Do you approve of President X?” These are of considerable valuable to pollsters, political campaigns, lobbyists, and the “news junkie” class but do not reveal patterns in naturally occurring discourse using the words and opinions of modal citizens.
The Need for Public Social Science
In an age of populism, we need to do more to connect with the general public that is increasingly turning to social media that lacks almost any and all gatekeepers. To provide accurate and relatable information to the public, we need research methods that allow us to study rich narrative data. And equally important we need comprehensible method with which to analyze digital political discourse in an increasingly complex media system.
Michael Burawoy understood this in his call for “public sociology” or better yet “public social science” to capture the allied and important fields of communication and media studies. As Burawoy explains, public sociology carries our research “beyond the academy” and is propelled by the challenge to “engage multiple publics in multiple ways” as a means of bridging “the growing gap between the sociological ethos and the world we study” and “the world of publics” (2005).
Public Social Science calls for us to invest in public-facing social sciences in which narrative, storytelling, and naturally occurring discourse are prominently featured because these are the vehicles of communication salient to the general public to a much greater degree than soundbites of presidential approval ratings on the news.
Interpretive Methodological Tools for a Contentious Age
Contrary to soundbites, for qualitative methodologists, an understanding of data comes from deep, often long-term immersion in data sets and communities. Indeed, the worth of these approaches has been validated by thirty years of research in which a variety of qualitative social scientists have attempted to study the digital public sphere.
As researchers have long made clear, participation in spaces reserved for political talk and discussion allows for deliberative debate fundamental to democracy (Lamont and Molnár 2002: 182). Whether eliciting a range of responses from those sharing ideological alignment or those with hostile views, the digital public sphere plays an important role in shaping public opinion. (Steinkuehler, 2002). Since these early studies, digital spaces have proven their worth as channels though which people can engage in reasoned argumentation on a number of topics of interest to public facing interpretive researchers in political campaigns, policy work, and lobbying just to name a few potential avenues for public-facing researchers.
Building on the concept of Public Social Science, this research begins to meet these needs by offering a toolkit for content analysis that allows us to analyze data interpretively in line with Public Social Science. This procedure yields nuanced understandings that capture the modal person’s imagination. This article offers content analysis strategies that can be deployed to bolster our claims and allows us to gain authority. In so doing, our findings may find greater traction to serve both the general public via Public Social Science and the larger social science community by demystifying inductive processes at the core of interpretive processes.
Interpretive Methods Now and Then
Why are these methods needed? Despite the explosion of qualitative data in digital political spaces, we still do not have an established toolkit with which to interpretively study such evidence. A toolkit is needed to identify the object of study with replicable operationalizations, sample data sets, and develop appropriate coding tools for interpretive objects of study. Therefore, this paper speaks to those with these aims and provides a content analysis toolkit is needed for those analyzing naturally occurring digital discourse, storytelling, and digital narratives.
Across these digital fieldsites, my aim was to capture interpretive data to build analytic frameworks for content analysis. Contrary to purely quantitative methods, this grounded theory approach eschews coming to the field or data with predetermined theories or hypotheses to be tested. Instead, my constant goal was to engage in participant observation through which I arrived “at theoretical propositions after having looked at the social world, not before” (Emerson, 1983: 93). In other words, my goal was to understand the views of the general public using their terminologies and frames of reference.
To see this need clearly, one only has to look at work on the COVID-19 crisis that presented such an opportunity for researchers to demonstrate the worth of interpretive methods. When we compare some excellent examples of this work from international perspectives (Georgiou and Titley, 2022; Orgad and Hegde, 2022; Chatzidakis and Littler, 2022), we see that the time-honored methods are still in use. Indeed, if we compare these interpretive studies of COVID-19 with seminal studies of online discourse as early as the late 1990s (Hill and Hughes, 1998), we find that traditional interpretive methods are employed in the analysis. Much like early interpretive studies included asynchronous or synchronous chat (Stromer-Galley, 2003), both early and later interpretive work relies on textual presentation of findings to describe a variety of issues from governmental messaging to participants engaging in political talk amongst individuals and groups unknown to one another ((Herring, 2003; Weger and Aackhus, 2003).
While valuable, such work does not provide an easily replicable formula for other interpretive researchers or a toolkit facilitating Public Social Science that is accessible to modal individuals, a need that is increasingly recognized by social scientists and policy makers alike (Schulz, Robinson, and Levine. 2022; Robinson, Trammel, and Moles. 2025).
Interpretive Analysis: A Playbook
Below fruitful content analysis strategies are revealed to sample and code qualitative data along differentiated axes. As we will see, this interpretive toolkit is useful for content analysis foci including but are not limited to: identification of salient topics as defined by participants, static versus changing views, interactional strategies between ordinary people staking out identify-affirming and solidarity-inducing exchanges with others, etc. Given the vast differences in these foci, for content analysis using an interpretive lens, differentiated coding schemes are needed to tease apart different kinds of sampling and coding from which to analyze data.
This toolkit can accompany cyberethnographies in which the researcher chooses a digital fieldsite and engages in participant observation over time. Often it is helpful to first act as a participant observer over time, observe ongoing interactions and discussions to ground the researcher’s authority in the fieldsite and the data. After “real time” participant observation of digital data, the researcher will have a better sense of how to make critical decisions on bounding the data set.
Rationales regarding start and end points are important but are also unique to each data set and project, thus indicating the need for participant observation as the first step. Therefore it is advisable to wait until data points or interactions have been archived so that the researcher can record them in a digital format that preserved the sequencing experienced by the participants. This also ensures that the researcher gains familiarity with the community in order to be able to ascertain “natural” start and end points to the interactions.
Open Coding and Probability Sampling
The first step is to review the data using open coding procedures to demarcate initial analytic categories and watch for these patterns as the researcher inductively codes the data. Subsequently, the researchers should engage in multiple rounds of code-and-recode to generate more nuanced categories as initial sensitizing devices. This allows the researcher to generate content analysis frequency counts for different levels of analysis content, interactions, and opinions
To ensure the accuracy of the codes across the data set, next, the researcher must generate appropriate random samples in order to assemble a comprehensive inventory of the objects of study. For example, one sample may be taken for themes (for example every nth datum) to capture the issues participants chose to address. Reviewing the sample, researchers should create an inventory that reflects dominant issues participants discussed.
By contrast, another sampling strategy may be more appropriate to capture interactions (for example every nth subject thread). This kind of sampling allows researchers to obtain a detailed inventory of interactions. Using this inventory, researchers can then produce frequency counts of interactions in each of the digital sites, which is ideal for comparison of positive and negative opinions.
While samples may be randomly selected using a random number table, they may also be generated with statistical packages such as R, STATA, SPSS, etc. Statistical packages require only the size of the universe to generate each of the random samples, without replacement. Most programs produce a unique identifier for each datum in data sets. However, in some cases the researcher may need to give each datum a unique identifier (such as an identifying number before pulling out the numbered posts generated by their statistical package of choice.
Once the researcher culls the numbered posts generated by this process, it will produce random samples per the parameters chosen by the researcher. This step assures readers that the author is accurately reporting the data set without bias or cherry picking.
Establishing Reliability and Quality Improvement of Inventory
I assigned binary codes (present/absent) to each issue or interaction. I generated a comprehensive list of issues and interactions in each the digital fieldsites. I then excluded any issue or interaction code that was not present in at least 1% of the data. I then assembled the list of issues and interactions from each of the communities into a master list of issues and interactions. To ensure reliability, I maintained broad issue and interaction codes.
After generating the inventory of issues, interactions, and codes, I tested its reliability by performing a series of code-recode tests. Beginning with the nth post in each subset of posts, I recoded every twentieth post for a second time. I noted any discrepancies between the two phases of coding. For each discrepancy, I examined the problematic category or code. I then examined other posts that had been labeled with the same categories or codes. I then refined these distinctions and rewrote the wording for the issues, categories, or codes as appropriate. Finally, I rigorously examined my coding categories through repeated rounds of code-recode to assure reliability.
Coding Exemplars: Inventory of Themes and Issues
Here I turn to my own ethnographies of online political discourse to offer examples. In this section, I show the steps that I took to come up with a comprehensive set of issues discussed by participants in the digital fieldsite. My interpretive work has used content analysis to probe naturally occurring data in online communities in Brazil, France, and the U.S. Foci have included political and ideological topics including but not limited to comparison of presidents and administrations in Brazil and the U.S., faith in public institutions, political violence, etc. (Robinson, 2022, 2021, 2017, and 2005). Turning to examples from my own work on political discourse, I have included examples below that elucidate the issue and interaction codes representative of those I applied to posts to generate a comprehensive inventory.
To generate the comprehensive inventory of the issues participants chose to address in my work comparing Brazilian, French, and American responses to political violence, I first read every post in the sample and then created an exhaustive list of issues addressed in the posts.
In the example below, I am coding discourse on political violence (9/11/01):
Example 1
My profound sympathies to Americans, especially New Yorkers, for this wanton destruction and loss of lives. My prayers go to all victims and their families during these trying times.
Theme Codes
Solidarity/Condolence: Yes
Religious/Spiritual/Prayer: Yes
Victim/Families: Yes
In the first example, I coded the post with the issue of Solidarity/Condolence: Yes based on the phrase “profound sympathies to Americans.” I derived the issue of Religious/Spiritual/Prayer: Yes based on the phrase “my prayers go.” I coded the issue of Victim/Families: Yes from the phrase “all victims and their families.”
Example 2
Fry the Taliban
Have you or anyone else read the article about the code words in the threat against the WH and Air Force One? That is why the Secret Service diverted the president’s plane on Tuesday when he wanted to return to Washington.
There are terrorist infiltrators who have apparently gotten hold of the highest top secret codes. And we’re supposed to wait for them to acquire nuclear weapons to use against us?
Theme Codes
Perpetrator: Yes
Future Course of Action: Yes
In the second example, I coded the issue of Perpetrator: Yes based on the phrase “there are terrorist infiltrators.” I coded the issue of Future Course of Action: Yes based on the phrase “Fry the Taliban.” I coded the interactive category based on the phrase “Have you or anyone else read the article.” I coded the category Asks Question: Yes based on this same phrase that asks a question.
Example 3
Dirty dog
So you would have been against our declaring war on Japan after Pearl Harbor. We have gone the restraint route and not only hasn’t it worked, it’s emboldened the terrorists. You don’t cure cancer by taking out a small part of it, you take all of it out. This is a cancer that must be wiped out.
These terrorists don’t think like you or I. They must be wiped off the face of the earth so that this will never happen again. Japan was brought to her knees and is now a peaceful country. You may be from a university but history obviously wasn’t your major. This is nothing short of war and you win a war by wiping out the enemy. Are you really that naive?
Theme Codes
Historical Reference: Yes
Perpetrator: Yes
Past Course of Action: Yes
Future Course of Action: Yes
I coded Example Post 3 with the Historical Reference: Yes code based on the phrase “declaring war on Japan after Pearl Harbor.” I coded the issue of Perpetrator: Yes based on the phrases “emboldened the terrorist” and “These terrorists don’t think like you or I.” I coded this post with the issue Past Course of Action: Yes based on the phrase “We have gone the restraint route and not only hasn’t it worked, it has emboldened the terrorists.” I coded the issue Future Course of Action: Yes based on the phrases “This cancer must be wiped out” and “They must be wiped off the face of the earth so that this will never happen again,” as well as “This is nothing short of war and you win a war by wiping out the enemy.”
Coding Exemplar: Inventory of Interactions
Using this identical sample, I then coded for interactivity, which is a different dimension than the themes discussed.
Example 1
My profound sympathies to Americans, especially New Yorkers, for this wanton destruction and loss of lives. My prayers go to all victims and their families during these trying times.
Interaction Code
Interactive: No
The first example had no interactions in it as it did not address another user or users. Therefore, I coded it as “Interactive: No.”
Example 2
Fry the Taliban
Have you or anyone else read the article about the code words in the threat against the WH and Air Force One? That is why the Secret Service diverted the president’s plane on Tuesday when he wanted to return to Washington.
There are terrorist infiltrators who have apparently gotten hold of the highest top secret codes. And we’re supposed to wait for them to acquire nuclear weapons to use against us?
Interaction Codes
Interactive: Yes
Asks Question: Yes
In the second example, I coded the interactive category based on the phrase “Have you or anyone else read the article.” I coded the category Asks Question: Yes based on this same phrase that asks a question.
Example 3
Dirty dog
So you would have been against our declaring war on Japan after Pearl Harbor. We have gone the restraint route and not only hasn’t it worked, it’s emboldened the terrorists. You don’t cure cancer by taking out a small part of it, you take all of it out. This is a cancer that must be wiped out.
These terrorists don’t think like you or I. They must be wiped off the face of the earth so that this will never happen again. Japan was brought to her
knees and is now a peaceful country. You may be from a university but history obviously wasn’t your major. This is nothing short of war and you win a war by wiping out the enemy. Are you really that naive?
Interaction Codes
Interactive: Yes
Critique: Yes
I coded this post as Interactive: Yes based on the reference to the user “Dirty dog” in the first line of the post indicating that Dirty dog’s previous post is addressed. I coded the post with Critique: Yes based on the phrases “You may be from a university but history obviously wasn’t your major” and “Are you really that naïve?”
Below Table 1 indicates how binary codes were assembled and deployed.
Coding Exemplar: Positive and Negative Interactions
Allowing for the quantification of qualitative data may be helpful for interpretive researchers to use a mixed method approach that allows for visual representation of change over time or other phenomena exhibiting change over time.
In my work comparing President Jair Bolsonaro and President Donald Trump and their administrations (2022), I used the same random sampling techniques described above to “develop a coding scheme that reflected dominant issues discussed and self-identified nationality. Once I had completed generating this inventory of issues and identifiers, I generated a second random sample. I used this second sample to obtain a detailed inventory of issues and identifiers and compared them across the three venues” (2021). I then formulated another coding scheme to measure the judgments made by participants about selected issues.
I ascertained whether people expressed negative or positive opinions about these categories. To do this, I applied the following codes that would measure positive and negative opinions while allowing measures for posts that contained both positive and negative opinions. I selected key issue categories including but not limited to those in the table below:
For example, evaluation coding was important for posts dealing with critique and affirmation of each president’s handling of the pandemic as a public health crisis. In this data, participants referred to policies and presidential administrations to make judgments on a number of public institutions. Examples 1, 2, and 3 represent negative opinions, while Examples 4 and 5 offer examples of positive opinions.
Example 1
Bolsonaro has been contradicting health experts since the pandemic started, advocating for a “vertical social isolation” which he invented and that has no scientific support nor evidence. Also, he said numerous times that Covid-19 is nothing more than “a little flu”. No health system can fight that amount of misinformation.
Interaction Code
Negative Opinion Yes (makes any negative evaluation)
Example 2
As a Brazilian, I would add that democracy is as good as the education of the people…in those countries in which education is performed recklessly, democracy often leads to dictatorship in so far as fascist tendencies include the idea of questioning science and the ones who have knowledge (a degree, some academic title). Here in Brazil, as well as at the United States people with no knowledge at all believe what they want regardless of proof, so it's easy to deceive and redirect them with fake news…
Interaction Code
Negative Opinion Yes (makes any negative evaluation)
Example 3
Brazil is going through its worst moment in political history since its independence, in spite of terrible moments like the dictatorship of 1964 that, incredibly, is defended by President Bolsonaro himself. I know that he was elected because of a wave of hatred coupled with an agreement between bad politicians and an extremely corrupt judiciary (in addition to the cooperation of the media outlets that were only concerned with selling scandals).
Interaction Code
Negative Opinion Yes (makes any negative evaluation)
Example 4
I think Bolsonaro is a super president.
Interaction Code
Positive Opinion Yes (makes any positive evaluation)
Example 5
Bolsonaro is great president who speaks for the people. He is consistently both ethical and honest.
Interaction Code
Positive Opinion Yes (makes any positive evaluation)
Visualizations
Finally, coding the samples from different time intervals can reveal change over time. For example, below I show how interactivity increased over time by coding both posts and strings. I then performed a validity and reliability check by subsequently spot-checking threads from each community to see if there was any reason to take a larger sample. The findings from the spot checks produced consistent findings, therefore I stopped sampling.
Coding for interactivity allowed me to compare interactions over time in my work on opinions surrounding political violence (Robinson, 2005). The examples below show the comparison of interactivity across national sites of relative contribution over time.
Conclusions: Tools for Public Social Science in the Digital Age
As we have seen, digital fieldsites are fruitful venues to study online exchange or “talk” about important political issues that allow for meaningful interactions meriting interpretive work. Indeed, as recent elections in the U.S. have shown (Robinson, 2024) for younger generations such as Gen Z, digital platforms have become their central source of news and opinion exchange.
Therefore, now more than ever, strategies are needed for public-facing social sciences in which narrative, storytelling, and naturally occurring discourse are prominently featured on sites such as TikTok.
This research begins to fill this gap by offering content analysis strategies for such interpretively oriented qualitative researchers. In providing replicable methodological tools to analyze data interpretively, this research provides a toolkit for qualitative researchers engage in Public Social Science in an age of contentious politics in which young voters hold increasing power that they express in digital venues. Those who may doubt the need for such work should turn their attention to the 2024 Presidential election in the U.S. as I argued in my work on Gen Z (2024):
The growing digital conservative ecosystem exercises “an effect on many voters, particularly members of Generation Z” such that “by the time that former President Donald J. Trump joined TikTok in June, conservative content was flourishing on the social media platform” (Janfaza, 2024) used by over 60 percent of those under 30 (Eddy, 2024). The rise of conservative influencers points to the need for future work on digital media prosumption as a vehicle for the [de]construction of hypergendered (and hyercontested) identities along highly fractured and ideological line ... digital media increasingly is window through which Gen Z generates, co-opts, and/or repudiates a number of identities and identity options. The impact of ideological digital content is clear: “contemporary political ills ... are often attributed to increasing polarization and partisan tribalism ... many such problems are less the product of left-right orientations than an orthogonal ‘anti-establishment’ dimension of opinion ...” (Uscinski, et al., 2021, p. 877).
Unpacking these timely issues is critical to the advancement of Public Social Science that brings our work “…into dialogue with audiences beyond the academy, an open dialogue in which both sides deepen their understanding of public issues . . .’ (Burawoy, 2005). In closing, the time is ripe for interpretive social scientists to exploit the strategies laid out here in the service of Public Social Science. It is imperative that we adopt these forms of practice for multiple reasons. Not only do they offer a means for social scientists to share their work with the general public but also moves beyond unidirectional dissemination of knowledge (Carrigan & Fatsis, 2021, p. 7) but they also advance our voice with public-facing entities including policy makers and government bureaucracies (Schulz et al., 2022).
Author Acknowledgments
The author thanks the Professor Sonia Virgínia Moreira for her unstinting leadership of the Brazil-U.S. Colloquium on Communication Studies that provides a vibrant home for scholarly exchange. The author also thanks Intercom for the generous support of the Colloquium that has made a difference in the lives of so many scholars over the years.
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Editing and XML Markup:
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How to cite:
ROBINSON, Laura. Tools for public social science in the digital age: interpretative strategies for content analysis in the public good. São Paulo: INTERCOM - Brazilian Journal of Communication Sciences, v. 48, e2025121. https://doi.org/10.1590/1809-58442025121.
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Article submitted to similarity check
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INTERCOM Journal encourages data sharing but, in compliance with ethical guidelines, does not require disclosure of any means of identifying research subjects, thereby preserving their privacy. The practice of open data aims to enable reproducibility of results and ensure unrestricted transparency of published research outcomes, without requiring the identity of research subjects.
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Funding
CNPq and CAPES
Data Availability
All data underlying this article are contained within the body of the text.
References
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» http://www.albany.edu/~jstromer/aoir2004.pdf - Weger, Harry J. and Aackhus, Mark. 2003. Arguing in Internet Chat Rooms: Argumentative Adaptations to Chat Room Design and some Consequences for Public Deliberation at a Distance. Argumentation and Advocacy
Edited by
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Editors Responsible for the reception, desk review, and evaluation process:
Ana Paula Goulart de Andrade (UFRRJ) and Jorge Carlos Felz Ferreira (UFJF)
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Editors-in-Chief:
Dr. Marialva BarbosaFederal University of Rio de Janeiro, UFRJDr. Sonia Virginia MoreiraRio de Janeiro State University, UERJ
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Executive Editors:
Dr. Jorge C. Felz FerreiraFederal University of Juiz de Fora, UFJFDr. Ana Paula Goulart de AndradeFederal Rural University of Rio de Janeiro
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Associate Editor:
Dr. Sandro Torres de AzevedoFederal University of Rio de Janeiro, UFRJ
