Tag Archives: text analysis

I think I’m using “big data” incorrectly

I think I’m using the term “big data” incorrectly. When I talk about big data, I’m referring to the massive amount of freely available information that researchers can collect from the internet. My expectation is that the researchers must choose which firehose best fits their research goals, collect and store the data, and groom it to the point of usability before using it to answer targeted questions or examining it for answers in need of a question.

The first element of this that makes it “big data” to me, is that the data is freely available and not subject to any privacy violations. It can be difficult to collect and store, because of its sheer size, but it is not password protected. For this reason, I would not consider Facebook to be a source for “big data.” I believe that the overwhelming majority of Facebook users impose some privacy controls, and the resulting, freely available information cannot be assigned any kind of validity. There are plenty of measures of inclusion for online research, and ignorance about privacy rules or shear exhibitionism are not a target qualities by any of these standards.

The second crucial element to my definition of “big data” is structure. My expectation is that it is in any researchers interest to understand the genesis and structure of their data as much as possible, both for the sake of grooming, and for the sake of assigning some sense of validity to their findings. Targeted information will be layed out and signaled very differently in different online environments, and the researcher must work to develop both working delimiters to find probable working targets and a sense of context for the data.

The third crucial element is representativeness. What do these findings represent? Under what conditions? “Big data” has a wide array of answers to these questions. First, it is crucial to note that it is not representative of the general population. It represents only the networked members of a population who were actively engaging with an online interface within the captured window of time in a way that left a trace or produced data. Because of this, we look at individual people by their networks, and not by their representativeness. Who did they influence, and to what degree could they influence those people? And we look at other units of analysis, such as the website that the people were contributing on, the connectedness of that website, and the words themselves, and their degree of influence, both directly an indirectly.

Given those elements of understanding, we are able to provide a framework from which the analysis of the data itself is meaningful and useful.

I’m aware that my definition is not the generally accepted definition. But for the time being I will continue to use it for two reasons:

1. Because I haven’t seen any other terms that better fit
2. Because I think that it is critically important that any talk about data use is tied to measures that encourage the researcher to think about the meaning and value of their data

It’s my hope that this is a continuing discussion. In the meantime, I will trudge on in idealistic ignorance.

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.

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”

Could our attitude toward marketing determine our field’s future?

In our office, we call it the “cocktail party question:” What do you do for a living? For those of us who work in the area of survey research, this can be a particularly difficult question to answer. Not only do people rarely know much about our work, but they rarely have a great deal of interest in it. I like to think of myself as a survey methodologist, but it is easier in social situations to discuss the focus of my research than my passion for methodology. I work at the American Institute of Physics, so I describe my work as “studying people who study physics.” Usually this description is greeted with an uncomfortable laugh, and the conversation progresses elsewhere. Score!

But the wider lack of understanding of survey research can have larger implications than simply awkward social situations. It can also cause tension with clients who don’t understand our work, our process, or where and how we add expertise to the process. Toward this end, I once wrote a guide for working with clients that separated out each stage in the survey process and detailed what expertise the researcher brings to the stage and what expertise we need from the client. I hoped that it would be a way of both separating and affirming the roles of client and researcher and advertising our firm and our field. I have not ye had the opportunity to use this piece, because of the nature of my current projects, but I’d be happy to share it with anyone who is interested in using or adapting it.

I think about that piece often as I see more talk about big data and social media analysis. Data seems to be everywhere and free, and I wonder what affect this buzz will have on a body of research consumers who might not have respected the role of the researchers from the get-go. We worried when Survey Monkey and other automated survey tools came along, but the current bevvy of tools and attitudes could have an exponentially larger impact on our practice.

Survey researchers often thumb their nose at advertising, despite the heavy methodological overlap. Oftentimes there is a knee-jerk reaction against marketing speak. Not only do survey methodologists often thumb their/our noses at the goal and importance of advertising, but they/we often thumb their/our nose at what appears to be evidence of less rigorous methodology. This has led us to a ridiculous point where data and analyses have evolved quickly with the demand and heavy use of advertising and market researchers and evolved strikingly little in more traditional survey areas, like polling and educational research. Much of the rhetoric about social media analysis, text analysis, social network analysis and big data is directed at the marketing and advertising crowd. Translating it to a wider research context and communicating it to a field that is often not eager to adapt to it can be difficult. And yet the exchange of ideas between the sister fields has never been more crucial to our mutual survival and relevance.

One of the goals of this blog has been to approach the changing landscape of research from a methodologically sound, interdisciplinary perspective that doesn’t suffer from the artificial walls and divisions. As I’ve worked on the blog, my own research methodology has evolved considerably. I’m relying more heavily on mixed methods and trying to use and integrate different tools into my work. I’ve learned quite a bit from researchers with a wide variety of backgrounds, and I often feel like I’m belted into a car with the windows down, hurtling down the highways of progress at top speed and trying to control the airflow. And then I often glimpse other survey researchers out the window, driving slowly, sensibly along the access road alongside the highway. I wonder if my mentors feel the change of landscape as viscerally as I do. I wonder how to carry forward the anchors and quality controls that led to such high quality research in the survey realm. I wonder about the future. And the present. About who’s driving, and who in what car is talking to who? Using what gps?

Mostly I wonder: could our negative attitude toward advertising and market research drive us right into obscurity? Are we too quick to misjudge the magnitude of the changes afoot?

 

This post is meant to be provocative, and I hope it inspires some good conversation.

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.

Do you ever think about interfaces? Because I do. All the time.

Did you ever see the movie Singles? It came out in the early 90s, shortly before the alternative scene really blew up and I dyed [part of] my hair blue and thought seriously about piercings. Singles was a part of the growth of the alternative movement. In the movie, there is a moment when one character says to another “Do you ever think about traffic? Because I do. All the time.” I spent quite a bit of time obsessing over that line, about what it meant, and, more deeply, what it signaled.

I still think about that line. As I drove toward the turnoff to my mom’s street during our 4th of July vacation, I saw what looked like the turn lane for her street, but it was actually an intersection- less left- turning split immediately preceding the real left turn lane for her street. It threw me off every time, and I kept remembering that romantic moment in Singles when the two characters were getting to know each other’s quirks, and the man was talking about traffic. And it was okay, even cool, to be quirky and think or talk about traffic, even during a romantic moment.

I don’t think about traffic often. But I am no less quirky. Lately, I tend to think about interfaces. Before my first brush with NLP (Natural Language Processing), I thought quite a bit about alternatives to e-mail. Since I discovered the world of text analytics, I have been thinking quite a bit about ways to integrate the knowledge across different fields about methods for text analysis and the needs of quantitative and qualitative researchers. I want to think outside of the sentiment box, because I believe that sentiment analysis does not fully address the underlying richness of textual data. I want to find a way to give researchers what they need, not what they think they want. Recently, my thinking on this topic has flipped. Instead of thinking from the data end, or the analytic possibilities end, or about what programs already exist and what they do, I have started to think about interfaces. This feels like a real epiphany. Once we think about the problem from an interface, or user experience perspective, we can better utilize existing technology and harness user expectations.

Have you read the new Imagine book about how creativity works? I believe that this strategy is the natural step after spending time zoning out on the web, thinking, or not thinking, about research. The more time you cruise, the better feel you develop for what works and what doesn’t, the more you learn what to expect. Interfaces are simply the masks we put on datasets of all sorts. The data could be the world wide web as a whole, results from a site or time period, a database of merchandise, or even a set of open ended survey responses. The goal is to streamline the searching interface and then make it available for use on any number of datasets. We use NLP every day when we search the internet, or shop. We understand it intuitively. Why don’t we extend that understanding to text analysis?

I find myself thinking about what this interface should look like and what I want this program to do.

Not traffic, not as romantic. But still quirky and all-encompassing.

Question Writing is an Art

As a survey researcher, I like to participate in surveys with enough regularity to keep current on any trends in methodology. As a web designer, an aspect of successful design is a seamlessness with the visitor’s expectations. So if the survey design realm has moved toward submit buttons on the upper right hand corner of individual pages, your idea (no matter how clever) to put a submit button on the upper left can result in a disconnect on the part of the user that will effect their behavior on the page. In fact, the survey design world has evolved quite a bit in the last few years, and it is easy to design something that reflects poorly on the quality of your research endeavor. But these design concerns are less of an issue than they have been, because most researchers are using templates.

Yet there is still value in keeping current.

And sometimes we encounter questions that lend themselves to an explanation of the importance of question writing. These questions are a gift for a field that is so difficult to describe in terms of knowledge and skills!

Here is a question I encountered today (I won’t reveal the source):

How often do you purchase potato chips when you eat out at any quick service and fast food restaurants?

2x a week or more
1x a week
1x every 2-3 weeks
1x a month
1x every 2-3 months
Less than 1x every 3 months
Never

This is a prime example of a double barreled question, and it is also an especially difficult question to answer. In my care, I rarely eat at quick service restaurants, especially sandwich places, like this one, that offer potato chips. When I do eat at them, I am tempted to order chips. About half the time I will give in to the temptation with a bag of sunchips, which I’m pretty sure are not made of potato.

In bigger firms that have more time to work through, this information would come out in the process of a cognitive interview or think aloud during the pretesting phase. Many firms, however, have staunchly resisted these important steps in the surveying process, because of their time and expense. It is important to note that the time and expense involved with trying to make usable answers out of poorly written questions can be immense.

I have spent some time thinking about alternatives to cognitive testing, because I have some close experience with places that do not use this method. I suspect that this is a good place for text analytics, because of the power of reaching people quickly and potentially cheaply (depending on your embedded TA processes). Although oftentimes we are nervous about web analytics because of their representativeness, the bar for representativeness is significantly lower in the pretesting stage than in the analysis phase.

But, no matter what pretesting model you choose, it is important to look closely at the questions that you are asking. Are you asking a single question, or would these questions be better separated out into a series?

How often do you eat at quick service sandwich restaurants?

When you eat at quick service restaurants, do you order [potato] chips?

What kind of [potato] chips do you order?

The lesson of all of this is that question writing is important, and the questions we write in surveys will determine the kind of survey responses we receive and the usability of our answers.

Facebook Measures Happiness in Status Updates?

From Flowing data:

http://flowingdata.com/2009/10/05/facebook-measures-happiness-in-status-updates/

Does anyone have a link to the original report?

I really wish I had more of a window into the methodology of this one!

A couple of questions:

What is happiness?

How can it be measured or signaled? What kinds of data are representing happiness? Is this just an expanded or open ended sentiment analysis? Is the technology such that this would be a valid study?

Are Facebook statuses a sensible place to investigate happiness?

What is this study representing? Constituting? Perpetuating?

 

Edited to Add: http://blog.facebook.com/blog.php?post=150162112130