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Communication research has recently had an influx of groundbreaking findings based on big data. Examples include not only analyses of Twitter, Wikipedia, and Facebook, but also of search engine and smartphone uses. These can be put together under the label “digital media.” This article reviews some of the main findings of this research, emphasizing how big data findings contribute to existing theories and findings in communication research, which have so far been lacking. To do this, an analytical framework will be developed concerning the sources of digital data and how they relate to the pertinent media. This framework shows how data sources support making statements about the relation between digital media and social change. It is also possible to distinguish between a number of subfields that big data studies contribute to, including political communication, social network analysis, and mobile communication. One of the major challenges is that most of this research does not fall into the two main traditions in the study of communication, mass and interpersonal communication. This is readily apparent for media like Twitter and Facebook, where messages are often distributed in groups rather than broadcast or shared between only two people. This challenge also applies, for example, to the use of search engines, where the technology can tailor results to particular users or groups (this has been labeled the “filter bubble” effect). The framework is used to locate and integrate big data findings in the landscape of communication research, and thus to provide a guide to this emerging area.

Article

People who communicate health and risk information are often trying to determine new and innovative ways to reach members of their target audience. Because of the nearly ubiquitous use of mobile phones among individuals in the United States and the continued proliferation of such devices around the world, communicators have turned to mobile as a possible channel for disseminating health information. Mobile health, often referred to as mHealth, uses mobile and portable devices to communicate information about health and to monitor health issues. Cell phones are one primary form of mHealth, with the use of cell phone features such as text messaging and mobile applications (apps) often used as a way to provide health information and motivation to target audience members. Text messaging, or short message service (SMS), is a convenient form for conveying health information, as most cell phone owners regularly send and receive text messages. mHealth offers benefits over other channels for communicating health information, such as convenience, portability, interactivity, and the ability to personalize or tailor messages. Additionally, mHealth has been found to be effective at changing attitudes and behaviors related to health. Research has found mobile to be a tool useful for promoting healthy attitudes and behaviors related to a number of topic areas, from increased sexual health to decreased alcohol consumption. Literature from health communication and research into mHealth can provide guidance for health communicators looking to develop an effective mHealth intervention or program, but possible concerns related to the use of mobile need to be considered, such as concerns about data security and participant privacy.

Article

The digital is now an integral part of everyday cultural practices globally. This ubiquity makes studying digital culture both more complex and divergent. Much of the literature on digital culture argues that it is increasingly informed by playful and ludified characteristics. In this phenomenon, there has been a rise of innovative and playful methods to explore identity politics and place-making in an age of datafication. At the core of the interdisciplinary debates underpinning the understanding of digital culture is the ways in which STEM (Science, Technology, Engineering and Mathematics) and HASS (Humanities, Arts and Social Science) approaches have played out in, and through, algorithms and datafication (e.g., the rise of small data [ethnography] to counteract big data). As digital culture becomes all-encompassing, data and its politics become central. To understand digital culture requires us to acknowledge that datafication and algorithmic cultures are now commonplace—that is, where data penetrate, invade, and analyze our daily lives, causing anxiety and seen as potentially inaccurate statistical captures. Alongside the use of big data, the quantified self (QS) movement is amplifying the need to think more about how our data stories are being told and who is doing the telling. Tensions and paradoxes ensure—power and powerless; tactic and strategic; identity and anonymity; statistics and practices; and big data and little data. The ubiquity of digital culture is explored through the lens of play and playful resistance. In the face of algorithms and datafication, the contestation around playing with data takes on important features. In sum, play becomes a series of methods or modes of critique for agency and autonomy. Playfully acting against data as a form of resistance is a key method used by artists, designers, and creative practitioners working in the digital realm, and they are not easily defined.