This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams. Online harassment goes underreported due to the reliance on humans to identify and report harassment, reporting that is further slowed by requirements to fill out forms providing context. In addition, the time for moderators to respond and apply human judgment can take days, but response times in terms of minutes are needed in the online context. Though some of the major social media companies have been doing proprietary work in automating the detection of harassment, there are few tools available for use by the public. In addition, the amount of labeled online harassment data and availability of cross platform online harassment datasets is limited. We present the methodology used to create a harassment dataset and classifier and the dataset used to help the system learn what harassment looks like.

Because in these days of economic troubles, all areas of science, social science, and industry must account for and justify more closely what they do and want to do, students of technical writing will receive immediate, practical benefit from learning the theory and practice of writing proposals. Proposals are also marvelously versatile for the teacher, because they can be taught in courses of varying length, and to both homogeneous and heterogeneous groups of students. The greatest advantage in teaching them is that through the various parts of a standard proposal, practically every theoretical and/or expository technique used in technical writing can be discussed and practiced. Indeed the proposal can become in itself a minicourse in technical writing, creating yet another possible avenue for work: private consulting.

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