International Fanworks Day 2017 Is Coming

International Fanworks Day will take place on February 15, 2017. The OTW is making plans to celebrate, but we also want to know what you will be doing!

What is International Fanworks Day?

A day to promote fan creativity in all of its forms, all over the world. Whether in text, image, audio, or multimedia, and whatever their nation or language of origin, we use fanworks to express love for our fandoms and forge our own communities and traditions. On International Fanworks Day (IFD), we want fans everywhere to show how important fanworks are to them.

Tell Us What Fanworks Mean to You

We will be announcing OTW-sponsored activities next month, but in the meantime we’d like to hear your plans for celebrating. We’d also like to get you to tell us what fanworks mean to you.

Send us your thoughts (up to 500 words) through our Communications contact form by January 31. We will be selecting up to six submissions for publication on OTW News in February as part of our lead-up to International Fanworks Day. When submitting, please tell us:

  1. How you would like your name/pseudonym listed
  2. What country you call home

Submissions are welcome in all languages!

5 Things an OTW Volunteer Said

Five Things ChelseaIBelieve Said

Every month or so the OTW will be doing a Q&A with one of its volunteers about their experiences in the organization. The posts express each volunteer’s personal views and do not necessarily reflect the views of the OTW or constitute OTW policy. Today’s post is with ChelseaIBelieve, who volunteers as a staffer for the Tag Wrangling Committee

How does what you do as a volunteer fit into what the OTW does?

As a tag wrangler, I take all the tags people use on their works and make sure they’re sorted properly and link them together wherever possible. Wranglers assign themselves to fandoms that they have a good knowledge of the canon. For myself, I mostly work with Bandom fandoms and Sports fandoms.

I’m also a member of the tag wrangling staff. This means that in addition to my normal tag wrangling duties, I also work to help oversee the training and tracking of all of our wranglers along with taking care of some of the more difficult tasks we come across. We help guide the new wranglers and check-in on their progress often in their first few months to make sure everything is going smoothly with them. Once they get past training, staff members still check in on each wrangler regularly and work to answer any questions or concerns that come up. Staff members work on different projects depending on what we’re working on at that time, which can include putting together newsletters, keeping minutes from our staff meetings, and sorting through new wrangling applications.

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OTW Guest Post

OTW Guest Post: Smitha Milli

From time to time, the OTW will be hosting guest posts on our OTW News accounts. These guests will be providing an outside perspective on the OTW or aspects of fandom where our projects may have a presence. The posts express each author’s personal views and do not necessarily reflect the views of the OTW or constitute OTW policy. We welcome suggestions from fans for future guest posts, which can be left as a comment here or by contacting us directly.

Smitha Milli is a 4th-year undergraduate at UC Berkeley whose research interests lie in artificial intelligence and cognitive science. Today, Smitha talks about her research using natural language processing to reveal patterns in fanfiction texts, the results of which is available online.

How did you come to work with fanfiction in your research?

At the time I started this project my main research focus was in natural language processing (NLP). Natural language processing is a subfield of artificial intelligence that is concerned with creating algorithms to process and understand language. If you’ve ever used Google Translate or Siri, you’ve used products that depend on NLP research!

In addition to having many commercial applications, NLP can also be used as a tool to explore literature. People have automatically tracked dynamic relationships between characters, created computational models of literary character, and analyzed the change in emotional content over the course of a story. However, a bottleneck to improving algorithms in the literary domain was the lack of a large-scale dataset of modern literature. I originally started looking into fanfiction as a source for this kind of data. As I looked further, I found that the structure of fanfiction also made it possible to define interesting, new problems for NLP and I became interested in computationally analyzing social science questions about fanfiction.

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