I’ve been trying to wrap my head around social media research for a couple of years now. I don’t think it would be as hard to understand from any one academic or professional perspective, but, from an interdisciplinary standpoint, the variety of perspectives and the disconnects between them are stunning.
In the academic realm:
There is the computer science approach to social media research. From this standpoint, we see the fleshing out of machine learning algorithms in a stunning horserace of code development across a few programming languages. This is the most likely to be opaque, proprietary knowledge.
There is the NLP or linguistic approach, which overlaps to some degree with the cs approach, although it is often more closely tied to grammatical rules. In this case, we see grammatical parsers, dictionary development, and api’s or shared programming modules, such as NLTK or GATE. Linguistics is divided as a discipline, and many of these divisions have filtered into NLP.
Both the NLP and CS approaches can be fleshed out, trained, or used on just about any data set.
There are the discourse approaches. Discourse is an area of linguistics concerned with meaning above the level of the sentence. This type of research can follow more of a strict Conversation Analysis approach or a kind of Netnography approach. This school of thought is more concerned with context as a determiner or shaper of meaning than the two approaches above.
For these approaches, the dataset cannot just come from anywhere. The analyst should understand where the data came from.
One could divide these traditions by programming skills, but there are enough of us who do work on both sides that the distinction is superficial. Although, generally speaker, the deeper one’s programming or qualitative skills, the less likely one is to cross over to the other side.
There is also a growing tradition of data science, which is primarily quantitative. Although I have some statistical background and work with quantitative data sets every day, I don’t have a good understanding of data science as a discipline. I assume that the growing field of data visualization would fall into this camp.
In the professional realm:
There are many companies in horseraces to develop the best systems first. These companies use catchphrases like “big data” and “social media firehose” and often focus on sentiment analysis or topic analysis (usually topics are gleaned through keywords). These companies primarily market to the advertising industry and market researchers, often with inflated claims of accuracy, which are possible because of the opacity of their methods.
There is the realm of market research, which is quickly becoming dependent on fast, widely available knowledge. This knowledge is usually gleaned through companies involved in the horserace, without much awareness of the methodology. There is an increasing need for companies to be aware of their brand’s mentions and interactions online, in real time, and as they collect this information it is easy, convenient and cost effective to collect more information in the process, such as sentiment analyses and topic analyses. This field has created an astronomically high demand for big data analysis.
There is the traditional field of survey research. This field is methodical and error focused. Knowledge is created empirically and evaluated critically. Every aspect of the survey process is highly researched and understood in great depth, so new methods are greeted with a natural skepticism. Although they have traditionally been the anchors of good professional research methods and the leaders in the research field, survey researchers are largely outside of the big data rush. Survey researchers tend to value accuracy over timeliness, so the big, fast world of big data, with its dubious ability to create representative samples, hold little allure or relevance.
The wider picture
In the wider picture, we have discussions of access and use. We see a growing proportion of the population coming online on an ever greater variety of devices. On the surface, the digital divide is fast shrinking (albeit still significant). Some of the digital access debate has been expanded into an understanding of differential use- essentially that different people do different activities while online. I want to take this debate further by focusing on discursive access or the digital representation of language ideologies.
The problem with such a wide spread of methods, needs, focuses and analytic traditions is that there isn’t enough crossover. It is very difficult to find work that spreads across these domains. The audiences are different, the needs are different, the abilities are different, and the professional visions are dramatically different across traditions. Although many people are speaking, it seems like people are largely speaking within silos or echo chambers, and knowledge simply isn’t trickling across borders.
This problem has rapidly grown because the underlying professional industries have quickly calcified. Sentiment analysis is not the revolutionary answer to the text analysis problem, but it is good enough for now, and it is skyrocketing in use. Academia is moving too slow for the demands of industry and not addressing the needs of industry, so other analytic techniques are not being adopted.
Social media analysis would best be accomplished by a team of people, each with different training. But it is not developing that way. And that, I believe, is a big (and fast growing) problem.