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Full-Text Articles in Social and Behavioral Sciences
“Uh Oh. Cue The [New] Mommy Wars”: The Ideology Of Combative Mothering In Popular U.S. Newspaper Articles About Attachment Parenting, Julia Moore, Jenna Abetz
“Uh Oh. Cue The [New] Mommy Wars”: The Ideology Of Combative Mothering In Popular U.S. Newspaper Articles About Attachment Parenting, Julia Moore, Jenna Abetz
Department of Communication Studies: Faculty Publications
Through critique of concordance, we argue that popular U.S. newspaper articles about attachment parenting perpetuate the ideology of combative mothering, where mothers are in continuous competition with one another over parenting choices. Specifically, article writers construct a new, singular metaphorical mommy war between pro-attachment parenting and anti-attachment parenting proponents by prepackaging attachment parenting and its debate, advocating for attachment parenting through instinct and science, and rejecting attachment parenting because of harm to children, relationships, and mothers. A minority of articles, however, avoided reifying this pro-/anti-attachment parenting mommy war by exploring the complexities of parenting beyond prepackaged philosophies. We explore the …
Using Textual Features To Predict Popular Content On Digg, Paul H. Miller
Using Textual Features To Predict Popular Content On Digg, Paul H. Miller
Department of English: Dissertations, Theses, and Student Research
Over the past few years, collaborative rating sites, such as Netflix, Digg and Stumble, have become increasingly prevalent sites for users to find trending content. I used various data mining techniques to study Digg, a social news site, to examine the influence of content on popularity. What influence does content have on popularity, and what influence does content have on users’ decisions? Overwhelmingly, prior studies have consistently shown that predicting popularity based on content is difficult and maybe even inherently impossible. The same submission can have multiple outcomes and content neither determines popularity, nor individual user decisions. My results show …