Exploring “Mass Surveillance” Through Computational Linguistic Analysis of Five Text Corpora

A lot of digital ink has been spilled on the issue of “mass surveillance,” in the aftermath of the Edward Snowden mass data leak of secret government communications intelligence (COMINT) documents in 2013. To explore some of the extant ideas, five text sets were collected: academic articles, mainstream journalistic articles, Twitter microblogging messages from a #surveillance hashtag network, Wikipedia articles in the one-degree “Mass_surveillance” page network, and curated original leaked government documents. These respective text sets were analyzed with Linguistic Inquiry and Word Count (LIWC) (by Pennebaker Conglomerates, Inc.) and NVivo 11 Plus (by QSR International, Inc.). Also, the text sets were analyzed through close (human) reading (except for the government documents that were treated in a non-consumptive way). Using computational text analytics, this author found text patterns within and across the five text sets that shed light on the target topic. There were also discoveries on how textual conventions affect linguistic features and informational contents.

Snacking Around the World

Social media platforms enable access to large image sets for research, but there are few if any non-theoretical approaches to image analysis, categorization, and coding. Based on two image sets labeled by the #snack hashtag (on Instagram), a systematic and open inductive approach to identifying conceptual image categories was developed, and unique research questions designed. By systematically categorizing imagery in a bottom-up way, researchers may (1) describe and assess the image set contents and categorize them in multiple ways independent of a theoretical framework (and its potential biasing effects); (2) conceptualize what may be knowable from the image set by the defining of research questions that may be addressed in the empirical data; (3) categorize the available imagery broadly and in multiple ways as a precursor step to further exploration (e.g., research design, image coding, and development of a research codebook). This work informs the exploration and analysis of mobile-created contents for open learning.

Exploring Identity-Based Humor in a #Selfies #Humor Image Set From Instagram

On a social level, identity humor may be pro-social, anti-social, or more often, both. The research in this chapter examined three basic research questions based on the study of social imagery: (1) What does identity-based humor look like in terms of a #selfie #humor- tagged image set from the Instagram photo-sharing mobile app? (2) What earlier findings and theories about humor apply to the more modern forms of mediated social humor? Is it possible to effectively apply the Humor Styles Model to the images from the #selfie #humor Instagram image set to better understand #selfie #humor? If so, what may be discoverable using this approach? and (3) What are some constructive and systematized ways to analyze social image sets in a naïve and emergent way using manual and computer-supported techniques?

Coding Online Learner Image and Multimedia Submissions for Assignment Fulfillment

In K12 and higher education, instructors have been eliciting student work in a variety of digital forms: text, audio, image, slideshow, video, and various combinations thereof. These files are uploaded to learning management systems, online training systems, online research suites (online survey systems), and learning applications; they are shared on content-sharing social media sites (with varying degrees of public access). Some are created on presentation sites, which enable the collation of the various media formats into coherent wholes (whether as voicethreads or slideshows or digital publications). While assignments are becoming richer, in many cases, the assessment tools for the work have not changed to accommodate the changes in modality. This chapter provides a light review of the literature, then a decomposition of how to create assessment rubrics for a variety of assignments involving submitted imagery and multimedia. The proposed draft assessment rubric provides a start for instructors, who are encouraged to define customizable parts of the rubric and to add unique requirements based on their local contexts and the requirements of the respective assignments.

Coding Digital Learning Objects for Adoption for Online Teaching and Learning

A general observation is that 20% of reusable learning objects (RLOs) are adopted at least for a time, but a majority of LOs are created (probably for local purposes), placed online, and not used at all by others. This work explores how digital learning objects (DLOs) may be coded for desirable features for local adoption and usage. This then explores how DLOs are actually designed with varying weights applied to the desirable DLO features of users. Finally, there is a gaps analysis between what inheritors of DLOs are looking for and what design and development teams and instructional designers actually create. If digital learning objects are to be more widely shared, having instructional designers and developers close the gap in LO work may be an important step. A main challenge involves a fundamental imbalance in incentives in the LO economy as currently practiced.

Engaging Technology-Based Manifestos Three Ways

Researchers today have a variety of ways to engage with their textual research data. Three main approaches include (1) manual method-based coding (with light computational supports), (2) Computer-Assisted Qualitative Data AnalysiS (CAQDAS)-supported manual coding (with data queries), and (3) machine reading and autocoding. To enable deeper understandings of data coding, exploration, and knowing, the above three approaches were applied in the above sequence to a corpus of technology-based manifestos. This work resulted in observations of different types of findable data from the three textual coding approaches, which may be used to inform research design.

See Ya!

The renunciation of U.S. citizenship is a non-trivial action, with far-reaching implications, for the individual, his / her social group, and even for the nation. While several U.S. government agencies collect information about this phenomenon, little actual data are publicly shared and mostly only through the U.S. Internal Revenue Service. Social media platforms—Twitter, Facebook, Flickr, Wikipedia, and Reddit (among others)—offer some insights about American renunciation of citizenship. From this targeted data, it is possible to design and collate a custom-made spatial-based dictionary (to run on LIWC2015) in order to automate the analysis of textual data about this phenomenon. This paper describes this process of creating a custom spatial-based dictionary, methods for pilot-testing the dictionary's efficacy (with “test” social media data sets, with experts, and with discovered insights about the target phenomenon), fresh space-based insights about American renunciation of citizenship, and future research directions.