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In recent years, Twitter use has skyrocketed, with 23% of online Americans on Twitter (Duggan, 2015). A surprising number of parents also turn to social media in search of parenting advice, information, and answers to questions – 75% of parents use social media, and 59% of parents report they have found parenting information while doing so (Duggan, et al, 2015). This makes it increasingly important that research on parenting includes social media as a potential source of that information.
This project considers a single case study, that of autism and vaccinations, as a first step to understanding the vast landscape of parenting information on social media. The case study was chosen because of its high salience as a topic in parenting, and more broadly in the American media environment (Nixon & Clarke, 2013; Smith, et al, 2008; Speers & Lewis, 2004). To do so, we have refined a method to identify and categorize parenting information on Twitter. We access the Twitter ‘garden hose’ (a random 10% of all Twitter content), and then scrape that content based on a purposive list of parenting websites and topics. This list is generated from three types of seed content: 1) Twitter handles (i.e., Twitter accounts that give parenting advice, like Parenting magazine); 2) hashtags (those related to parenting like #parenting, #kids, etc); and 3) key words (like parent, kids, my child, etc). For each Twitter handle, a random sample of 5,000 followers is also taken and their tweets scraped.
This corpus of content was then examined using topic modeling, which uses algorithms to sort words and phrases by their co-occurrence and relevance to one another. Initial results suggest that the frequency of discussing autism and vaccinations on these Twitter accounts varies dramatically, ranging from .6% of conversation on PBS Parents’ Twitter account to 15.7% on WebMD’s Twitter account. In general, conversation about autism and vaccines is more frequent than conversation about child illness, but less frequent than topics like school, child safety (things like car seats, recalls, etc), and child behavior (misbehavior, discipline, etc). Next analytic steps will include examining full tweets for both stance – if the tweet discusses the positive, negative, or neutral aspects of breastfeeding - and sentiment – what emotion is used in the tweet (positive, negative or neutral).