Category Archives: sociolinguistics

Repeating language: what do we repeat, and what does it signal?

Yesterday I attended a talk by Jon Kleinberg entitled “Status, Power & Incentives in Social Media” in Honor of the UMD Human-Computer Interaction Lab’s 30th Anniversary.

 

This talk was dense and full of methods that are unfamiliar to me. He first discussed logical representations of human relationships, including orientations of sentiment and status, and then he ventured into discursive evidence of these relationships. Finally, he introduced formulas for influence in social media and talked about ways to manipulate the formulas by incentivizing desired behavior and deincentivizing less desired behavior.

 

In Linguistics, we talk a lot about linguistic accommodation. In any communicative event, it is normal for participant’s speech patterns to converge in some ways. This can be through repetition of words or grammatical structures. Kleinberg presented research about the social meaning of linguistic accommodation, showing that participants with less power tend to accommodate participants with more power more than participants with more power accommodate participants with less power. This idea of quantifying social influence is a very powerful notion in online research, where social influence is a more practical and useful research goal than general representativeness.

 

I wonder what strategies we use, consciously and unconsciously, when we accommodate other speakers. I wonder whether different forms of repetition have different underlying social meanings.

 

At the end of the talk, there was some discussion about both the constitution of iconic speech (unmarked words assembled in marked ways) and the meaning of norm flouting.

 

These are very promising avenues for online text research, and it is exciting to see them play out.

Getting to know your data

On Friday, I had the honor of participating in a microanalysis video discussion group with Fred Erickson. As he was introducing the process to the new attendees, he said something that really caught my attention. He said that videos and field notes are not data until someone decides to use them for research.

As someone with a background in survey research, the question of ‘what is data?’ was never really on my radar before graduate school. Although it’s always been good practice to know where your data comes from and what it represents in order to glean any kind of validity from your work, data was unquestioningly that which you see in a spreadsheet or delimited file, with cases going down and variables going across. If information could be formed like this, it was data. If not, it would need some manipulation. I remember discussing this with Anna Trester a couple of years ago. She found it hard to understand this limited framework, because, for her, the world was a potential data source. I’ve learned more about her perspective in the last couple of years, working with elements that I never before would have characterized as data, including pictures, websites, video footage of interactions, and now fieldwork as a participant observer.

Dr Erickson’s observation speaks to some frustration I’ve had lately, trying to understand the nature of “big data” sets. I’ve seen quite a bit of people looking for data, any data, to analyze. I could see the usefulness of this for corpus linguists, who use large bodies of textual data to study language use. A corpus linguist is able to use large bodies of text to see how we use words, which is a systematically patterned phenomena that goes much deeper than a dictionary definition could. I could also see the usefulness of large datasets in training programs to recognize genre, a really critical element in automated text analysis.

But beyond that, it is deeply important to understand the situated nature of language. People don’t produce text for the sake of producing text. Each textual element represents an intentioned social action on the part of the writer, and social goals are accomplished differently in different settings. In order for studies of textual data to produce valid conclusions with social commentary, contextual elements are extremely important.

Which leads me to ask if these agnostic datasets are being used solely as academic exercises by programmers and corpus linguists or if our hunger for data has led us to take any large body of information and declare it to be useful data from which to excise valid conclusions? Worse, are people using cookie cutter programs to investigate agnostic data sets like this without considering the wider validity?

I urge anyone looking to create insight from textual data to carefully get to know their data.

A brave new vision of the future of social science

I’ve been typing and organizing my notes from yesterday’s dc-aapor event on the past, present and future of survey research (which I still plan to share soon, after a little grooming). The process has been a meditative one.

I’ve been thinking about how I would characterize these same phases- the past, present and future… and then I had a vision of sorts on the way home today that I’d like to share. I’m going to take a minute to be a little post apocalyptic and let the future build itself. You can think of it as a daydream or thought experiment…

The past, I would characterize as the grand discovery of surveys as a tool for data collection; the honing and evolution of that tool in conjunction with its meticulous scientific development and the changing landscape around it; and the growth to dominance and proliferation of the method. The past was an era of measurement, of the total survey error model, of social Science.

The present I would characterize as a rapid coming together, or a perfect storm that is swirling data and ideas and disciplines of study and professions together in a grand sweeping wind. I see the survey folks trudging through the wind, waiting for the storm to pass, feet firmly anchored to solid ground.

The future is essentially the past, turned on its head. The pieces of the past are present, but mixed together and redistributed. Instead of examining the ways in which questions elicit usable data, we look at the data first and develop the questions from patterns in the data. In this era, data is everywhere, of various quality, character and genesis, and the skill is in the sense making.

This future is one of data driven analytic strategies, where research teams intrinsically need to be composed of a spectrum of different, specialized skills.

The kings of this future will be the experts in natural language processing, those with the skill of finding and using patterns in language. All language is patterned. Our job will be to find those patterns and then to discover their social meaning.

The computer scientists and coders will write the code to extract relevant subsets of data, and describe and learn patterns in the data. The natural language processing folks will hone the patterns by grammar and usage. The netnographers will describe and interpret the patterns, the data visualizers will make visual or interactive sense of the patterns, the sociologists will discover constructions of relative social groupings as they emerge and use those patterns. The discourse analysts will look across wider patterns of language and context dependency. The statisticians will make formulas to replicate, describe and evaluate the patterns, and models to predict future behaviors. Data science will be a crucial science built on the foundations of traditional and nontraditional academic disciplines.

How many people does it take to screw in this lightbulb? It depends on the skills of the people or person on the ladder.

Where do surveys fit in to this scheme? To be honest, I’m not sure. The success of surveys seems to rest in part on the failure of faster, cheaper methods with a great deal more inherent error.

This is not the only vision possible, but it’s a vision I saw while commuting home at the end of a damned long week… it’s a vision where naturalistic data is valued and experimentation is an extension of research, where diversity is a natural assumption of the model and not a superimposed dynamic, where the data itself and the patterns within it determine what is possible from it. It’s a vision where traditional academics fit only precariously; a future that could just as easily be ruled out by the constraints of the past as it could be adopted unintentionally, where meaning makers rush to be the rigs in the newest gold rush and theory is as desperately pursued as water sources in a drought.

The Bones of Solid Research?

What are the elements that make research “research” and not just “observation?” Where are the bones of the beast, and do all strategies share the same skeleton?

Last Thursday, in my Ethnography of Communication class, we spent the first half hour of class time taking field notes in the library coffee shop. Two parts of the experience struck me the hardest.

1.) I was exhausted. Class came at the end of a long, full work day, toward the end of a week that was full of back to school nights, work, homework and board meetings. I began my observation by ordering a (badly needed) coffee. My goal as I ordered was to see how few words I had to utter in order to complete the transaction. (In my defense, I am usually relatively talkative and friendly…) The experience of observing and speaking as little as possible reminded me of one of the coolest things I’d come across in my degree study: Charlotte Linde, SocioRocketScientist at NASA

2.) Charlotte Linde, SocioRocketScientist at NASA. Dr Linde had come to speak with the GU Linguistics department early in my tenure as a grad student. She mentioned that her thesis had been about the geography of communication- specifically: How did the layout of an (her?) apartment building help shape communication within it?

This idea had struck me, and stayed with me, but it didn’t really make sense until I began to study Ethnography of Communication. In the coffee shop, I structured my fieldnotes like a map and investigated it in terms of zones of activities. Then I investigated expectations and conventions of communication in each zone. As a follow-up to this activity, I’ll either return to the same shop or head to another coffee shop to do some contrastive mapping.

The process of Ethnography embodies the dynamic between quantitative and qualitative methods for me. When I read ethnographic research, I really find myself obsessing over ‘what makes this research?’ and ‘how is each statement justified?’ Survey methodology, which I am still doing every day at work, is so deeply structured that less structured research is, by contrast, a bit bewildering or shocking. Reading about qualitative methodology makes it seem so much more dependable and structured than reading ethnographic research papers does.

Much of the process of learning ethnography is learning yourself; your priorities, your organization, … learning why you notice what you do and evaluate it the way you do… Conversely, much of the process of reading ethnographic research seems to involve evaluation or skepticism of the researcher, the researcher’s perspective and the researcher’s interpretation. As a reader, the places where the researcher’s perspective varies from mine is clear and easy to see, as much as my own perspective is invisible to me.

All of this leads me back to the big questions I’m grappling with. Is this structured observational method the basis for all research? And how much structure does observation need to have in order to qualify as research?

I’d be interested to hear what you think of these issues!

Unlocking patterns in language

In linguistics study, we quickly learn that all language is patterned. Although the actual words we produce vary widely, the process of production does not. The process of constructing baby talk was found to be consistent across kids from 15 different languages. When any two people who do not speak overlapping languages come together and try to speak, the process is the same. When we look at any large body of data, we quickly learn that just about any linguistic phenomena is subject to statistical likelihood. Grammatical patterns govern the basic structure of what we see in the corpus. Variations in language use may tweak these patterns, but each variation is a patterned tweak with its own set of statistical likelihoods. Variations that people are quick to call bastardizations are actually patterned departures from what those people consider to be “standard” english. Understanding “differences not defecits” is a crucially important part of understanding and processing language, because any variation, even texting shorthand, “broken english,” or slang, can be better understood and used once its underlying structure is recognized.

The patterns in language extend beyond grammar to word usage. The most frequent words in a corpus are function words such as “a” and “the,” and the most frequent collocations are combinations like “and the” or “and then it.” These patterns govern the findings of a lot of investigations into textual data. A certain phrase may show up as a frequent member of a dataset simply because it is a common or lexicalized expression, and another combination may not appear because it is more rare- this could be particularly problematic, because what is rare is often more noticeable or important.

Here are some good starter questions to ask to better understand your textual data:

1) Where did this data come from? What was it’s original purpose and context?

2) What did the speakers intend to accomplish by producing this text?

3) What type of data or text, or genre, does this represent?

4) How was this data collected? Where is it from?

5) Who are the speakers? What is their relationship to eachother?

6) Is there any cohesion to the text?

7) What language is the text in? What is the linguistic background of the speakers?

8) Who is the intended audience?

9) What kind of repetition do you see in the text? What about repetition within the context of a conversation? What about repetition of outside elements?

10) What stands out as relatively unusual or rare within the body of text?

11) What is relatively common within the dataset?

12) What register is the text written in? Casual? Academic? Formal? Informal?

13) Pronoun use. Always look at pronoun use. It’s almost always enlightening.

These types of questions will take you much further into your dataset that the knee-jerk question “What is this text about?”

Now, go forth and research! …And be sure to report back!

A fleet of research possibilities and a scattering of updates

Tomorrow is my first day of my 3rd year as a Masters student in the MLC program at Georgetown University. I’m taking the slowwww route through higher ed, as happens when you work full-time, have two kids and are an only child who lost her mother along the way.

This semester I will [finally] take the class I’ve been borrowing pieces from for the past two years: Ethnography of Communication. I’ve decided to use this opportunity do an ethnography of DC taxi drivers. My husband is a DC taxi driver, so in essence this research will build on years of daily conversations. I find that the representation of DC taxi drivers in the news never quite approximates what I’ve seen, and that is my real motivation for the project. I have a couple of enthusiastic collaborators: my husband and a friend whose husband is also a DC taxi driver and who has been a vocal advocate for DC taxi drivers.

I am really eager to get back into linguistics study. I’ve been learning powerful sociolinguistic methods to recognize and interpret patterning in discourse, but it is a challenge not to fall into the age old habit of studying aboutness or topicality, which is much less patterned and powerful.

I have been fortunate enough to combine some of my new qualitative methods with my more quantitative work on some of the reports I’ve completed over the summer. I’m using the open ended responses that we usually don’t fully exploit in order to tell more detailed stories in our survey reports. But balancing quantitative and qualitative methods is very difficult, as I’ve mentioned before, because the power punch of good narrative blows away the quiet power of high quality, representative statistical analysis. Reporting qualitative findings has to be done very carefully.

Over the summer I had the wonderful opportunity to apply my sociolinguistics education to a medical setting. Last May, while my mom was on life support, we were touched by a medical error when my mom was mistakenly declared brain dead. Because she was an organ donor, her life support was not withdrawn before the error was recognized. But the fallout from the error was tremendous. The problem arose because two of her doctors were consulting by phone about their patients, and each thought they were talking about a different patient. In collaboration with one of the doctors involved, I’ve learned a great amount about medical errors and looked at the role of linguistics in bringing awareness to potential errors of miscommunication in conversation. This project was different from other research I’ve done, because it did not involve conducting new research, but rather rereading foundational research and focusing on conversational structure.

In this case, my recommendations were for an awareness of existing conversational structures, rather than an imposition of a new order or procedure. My recommendations, developed in conjunction with Dr Heidi Hamilton, the chair of our linguistics department and medical communication expert, were to be aware of conversational transition points, to focus on the patient identifiers used, and to avoid reaching back or ahead to other patients while discussing a single patient. Each patient discussion must be treated as a separate conversation. Conversation is one of the largest sources of medical error and must be approached carefully is critically important. My mom’s doctor and I hope to make a Grand Rounds presentation out of this effort.

On a personal level, this summer has been one of great transitions. I like to joke that the next time my mom passes away I’ll be better equipped to handle it all. I have learned quite a bit about real estate and estate law and estate sales and more. And about grieving, of course. Having just cleaned through my mom’s house last week, I am beginning this new school year more physically, mentally and emotionally tired than I have ever felt. A close friend of mine has recently finished an extended series of chemo and radiation, and she told me that she is reveling in her strength as it returns. I am also reveling in my own strength, as it returns. I may not be ready for the semester or the new school year, but I am ready for the first day of class tomorrow. And I’m hopeful. For the semester, for the research ahead, for my family, and for myself. I’m grateful for the guidance of my newest guardian angel and the inspiration of great research.

A snapshot from a lunchtime walk

In the words of Sri Aurobindo, “By your stumbling the world is perfected”

Rethinking demographics in research

I read a blog post on the LoveStats blog today that referred to one of the most widely regarded critiques of social media research: the lack of demographic information.

In traditional survey research, demographic information is a critically important piece of the analysis. We often ask questions like “Yes 50% of the respondents said they had encountered gender harassment, but what is the breakdown by gender?” The prospect of not having this demographic information is a large enough game changer to cast the field of social media research into the shade.

Here I’d like to take a sidestep and borrow a debate from linguistics. In the linguistic subfield of conversation analysis, there are two main streams of thought about analysis. One believes in gathering as much outside data as possible, often through ethnographic research, to inform a detailed understanding of the conversation. The second stream is rooted in the purity of the data. This stream emphasizes our dynamic construction of identity over the stability of identity. The underlying foundation of this stream is that we continually construct and reconstruct the most important and relevant elements of our identity in the process of our interaction. Take, for example, a study of an interaction between a doctor and a patient. The first school would bring into the analysis a body of knowledge about interactions between doctors and patients. The second would believe that this body of knowledge is potentially irrelevant or even corrupting to the analysis, and if the relationship is in fact relevant it will be constructed within the excerpt of study. This begs the question: are all interactions between doctors and patients primarily doctor patient interactions? We could address this further through the concept of framing and embedded frames (a la Goffman), but we won’t do that right now.

Instead, I’ll ask another question:
If we are studying gender discrimination, is it necessary to have a variable for gender within our datasouce?

My kneejerk reaction to this question, because of my quantitative background, is yes. But looking deeper: is gender always relevant? This does strongly depend on the datasource, so let’s assume for this example that the stimulus was a question on a survey that was not directly about discrimination, but rather more general (e.g. “Additional Comments:”).

What if we took that second CA approach, the purist approach, and say that where gender is applicable to the response it will be constructed within that response. The question now becomes ‘how is gender constructed within a response?’ This is a beautiful and interesting question for a linguist, and it may be a question that much better fits the underlying data and provides deeper insight into the data. It also turns the age old analytic strategy on its head. Now we can ask whether a priori assumptions that the demographics could or do matter are just rote research or truly the productive and informative measures that we’ve built them up to be?

I believe that this is a key difference between analysis types. In the qualitative analysis of open ended survey questions, it isn’t very meaningful to say x% of the respondents mentioned z, and y% of the respondents mentioned d, because a nonmention of z or d is not really meaningful. Instead we go deeper into the data to see what was said about d or z. So the goal is not prevalence, but description. On the other hand, prevalence is a hugely important aspect of quantitative analysis, as are other fun statistics which feed off of demographic variables.

The lesson in all of this is to think carefully about what is meaningful information that is relevant to your analysis and not to make assumptions across analytic strategies.

To go big, first think small

We use language all of the time. Because of this, we are all experts in language use. As native speakers of a language, we are experts in the intricacies of that language.

Why, then, do people study linguistics? Aren’t we all linguists?

Absolutely not.

We are experts in *using* language, but we are not experts in the methods we employ. Believe it or not, much of the process of speaking and hearing is not conscious. If it was, we would be sensorally overwhelmed with the sheer volume of words around us. Instead, listening comprehension involves a process of merging what we expect to hear with what we gauge to be the most important elements of what we do hear. The process of speaking involves merging our estimates of what the people we communicate with know and expect to hear with our understanding of the social expectations surrounding our words and our relationships and distilling these sources into a workable expression. The hearer will reconstruct elements of this process using cues that are sometimes conscious and sometimes not.

We often think of language as simple and mechanistic, but it is not simple at all. As conversational analysts, our job is to study conversation that we have access to in an attempt to reconstruct the elements that constituted the interaction. Even small chunks of conversation encode quite a bit of information.

The process of conversation analysis is very much contrary to our sense of language as regular language users. This makes the process of explaining our research to people outside our field difficult. It is difficult to justify the research, and it is difficult to explain why such small pieces of data can be so useful, when most other fields of research rely on greater volumes of data.

In fact, a greater volume of data can be more harmful than helpful in conversation analysis. Conversation is heavily dependent on its context; on the people conversing, their relationship, their expectations, their experiences that day, the things on their mind, what they expect from each other and the situation, their understanding of language and expectations, and more. The same sentence can have greatly different meanings once those factors are taken into account.

At a time when there is so much talk of the glory of big data, it is especially important to keep in mind the contributions of small data. These contributions are the ones that jeopardize the utility and promise of big data, and if these contributions can be captured in creative ways, they will be the true promise of the field.

Not what language users expect to see, but rather what we use every day, more or less consciously.

What is Data? The answer might surprise you

I like to compare my discovery of Sociolinguistics to my love of swimming. I like to consider myself a competent swimmer, and I love being underwater. But discovering sociolinguistics was like coming up for air and noticing air and dry land. A fundamental element that led to this feeling is the difference in data.

In survey research, we rarely think about what data looks like, unless we are training new hires for jobs like data entry. Data can be visualized as a spreadsheet. Each line is a case, and each column is a variable. The variables can be numeric or character and vary in size. We analyze the numbers using statistics and the character variables using qualitative analysis. Or, we can try quantitative techniques on character fields.

The field of survey research has been feeling out its edges increasingly in the past few years. This has led us to consider new data sources, particularly data sources that do not come from surveys. Two factors shape this exploration

1.) Consideration for the genesis and representativeness of the new data. What is it, and what does it represent?

2.) A sense of what data should look like. We expect new data to resemble old data. We think in terms of joining files; collating, concatenating, merging, aggregating and disaggregating. New data should look and work like this. And so our questions are more along the lines of: how can we make new data look like (or work with) old data?

Sociolinguistics could not be more different, in terms of data. In sociolinguistics, everything is data. Look around you: you’re looking at data. Listen: you’re listening to data. The signs that you passed on your way into work? Data. The tv shows you watch when you get home? Data. Cooking with recipes? Data. Talking on the phone? Data. Attending a meeting? Institutional discourse!

In sociolinguistics, we  call analytic methods our ‘toolkit,’ and we pride ourselves on being able to analyze any kind of data with that toolkit. We include ethnographic methods, visual semiotics, discourse methods, action-based studies, as well as traditional linguistic means and measures. Each of these methods can be addressed quantitatively or qualitatively. The best studies use a combination of quantitative and qualitative methods. To me, these methods and data sources are nothing short of mind blowing, and they redefine the prospect of social science research.