Читать книгу An Introduction to Text Mining - Gabe Ignatow - Страница 22

Having introduced text mining and text analysis, in this section we review some lessons that have been learned from other fields about how best to adapt social science research methods to data from online environments. This section is short but critically important for students who plan to perform research with data taken from social media platforms and websites.

Methodologies such as text mining that analyze data from digital environments offer potential cost- and time-efficiency advantages over older methods (Hewson & Laurent, 2012; Hewson, Yule, Laurent, & Vogel, 2003), as the Internet provides ready access to a potentially vast, geographically diverse participant pool. The speed and global reach of the Internet can facilitate cross-cultural research projects that would otherwise be prohibitively expensive. It also allows for the emergence of patterns of social interactions, which are elaborate in terms of their richness of communication exchange but where levels of anonymity and privacy can be high. The Internet’s unique combination of digital archiving technologies and users’ perceptions of anonymity and privacy may reduce social desirability effects (where research participants knowingly or unknowingly attempt to provide researchers with socially acceptable and desirable, rather than accurate, information). The unique attributes of Internet-based technologies may also reduce biases resulting from the perception of attributes such as race, ethnicity, and sex or gender, promoting greater candor. The convenience of these technologies can also empower research participants by allowing them to take part in study procedures that fit their schedules and can be performed within their own spaces such as at home or in a familiar work environment.

While Internet-based research has many advantages (see Hewson, Vogel, & Laurent, 2015), Internet-based data have a number of serious drawbacks for social science research. One major disadvantage is the potentially biased nature of Internet-accessed data samples. Sample bias is one of the most fundamental and difficult to manage challenges associated with Internet-mediated research (see Chapter 5). Second, as compared to offline methods, Internet-based data are often characterized by reduced levels of researcher control. This lack of control arises mainly from technical issues, such as users’ different hardware and software configurations and network traffic performance. Research participants working with different hardware platforms, operating systems, and browsers may experience social media services and online surveys very differently, and it is often extremely difficult for researchers to fully appreciate differences in participants’ experiences. In addition, hardware and software failures may lead to unpredicted effects, which may cause problems. Because of the lack of researcher presence, in Internet-based research there is often a lack of researcher control over and knowledge of variations in participants’ behaviors and the participation context. This may cause problems related to the extent to which researchers can gauge participants’ intentions and levels of sincerity and honesty during a study, as researchers lack nonverbal cues to evaluate participants compared with face-to-face communication.

Despite these weaknesses, scholars have long recognized digital technologies’ potential as research tools. While social researchers have occasionally developed brand-new Internet-based methodologies, they have also adapted preexisting research methods for use with evolving digital technology. Because a number of broadly applicable lessons have been learned from these adaptation processes, in the remainder of this chapter we briefly review some of the most widely used social science research methods that have been adapted to Internet-related communication technologies and some of the lessons learned from each. We discuss offline and online approaches to social surveys, ethnography, and archival research but do not cover online focus groups (Krueger & Casey, 2014) or experiments (Birnbaum, 2000). While focus groups and experiments are both important and widely used research methods, we have found that the lessons learned from developing online versions of these methods are less applicable to text mining than lessons learned from the former three.