1. Poster Print Size: This poster template is 24” high by 36” wide. It can be used to print any poster with a 2:3 aspect ratio including 36x54 and 48x72. Placeholders: The various elements included in this poster are ones we often see in medical, research, and scientific posters. Feel free to edit, move, add, and delete items, or change the layout to suit your needs. Always check with your conference organizer for specific requirements. Image Quality: You can place digital photos or logo art in your poster file by selecting the Insert, Picture command, or by using standard copy & paste. For best results, all graphic elements should be at least 150-200 pixels per inch in their final printed size. For instance, a 1600 x 1200 pixel photo will usually look fine up to 8“-10” wide on your printed poster. To preview the print quality of images, select a magnification of 100% when previewing your poster. This will give you a good idea of what it will look like in print. If you are laying out a large poster and using half- scale dimensions, be sure to preview your graphics at 200% to see them at their final printed size. Please note that graphics from websites (such as the logo on your hospital's or university's home page) will only be 72dpi and not suitable for printing. [This sidebar area does not print.] Change Color Theme: This template is designed to use the built-in color themes in the newer versions of PowerPoint. To change the color theme, select the Design tab, then select the Colors drop-down list. The default color theme for this template is “Office”, so you can always return to that after trying some of the alternatives. Printing Your Poster: Once your poster file is ready, visit www.genigraphics.com to order a high-quality, affordable poster print. Every order receives a free design review and we can delivery as fast as next business day within the US and Canada. Genigraphics® has been producing output from PowerPoint® longer than anyone in the industry; dating back to when we helped Microsoft® design the PowerPoint® software. US and Canada: 1-800- 790-4001 Email: info@genigraphics.com [This sidebar area does not print.] Automated Bias Detection in Journalism 1. T. Yano, P. Resnik, and N. Smith. 2010. Shedding (a thousands points of) light on biased language. 2. Y. R. Lin, J. P. Bagrow, and D. Lazer. 2011. More than ever? Quantifying media bias in networks. 3. L. Herzig, A. Nunes, and B. Snir. 2011. An annotation scheme for automated bias detection in Wikipedia. 4. R. González-Ibáñez, S. Muresan, N. Wacholder. Identifying sarcasm in Twitter: A closer look. 5. https://en.wikipedia.org/wiki/Bag-of-words_model References Be able to detected bias from parallel corpuses (collections of machine-readable texts) To the best of our knowledge, automated bias detection has never been done. Previous research in NLP, eg, annotation systems and sarcasm detection. Applications in journalism, politics, rhetoric, linguistics, law, education. Increased objectivity in news articles and educational resources. Assisted grading and bias-flagging. MOTIVATION Used existing software to streamline design : AlchemyAPI for simple sentiment analysis SVM light handles classification of texts: In lay terms, it does the guess work, provided it is given sufficient training data (patterns to build off). Bag of words approach; all the unique words in a document are given an index. The documents are then reconstructed from these indexes as vectors. For example: John likes to watch movies. Mary likes too. John also likes to watch football games. {"John": 1, "likes": 2, "to": 3, "watch": 4, "movies": 5, "also": 6, "football": 7, "games": 8, "Mary": 9, "too": 10} [1, 2, 1, 1, 1, 0, 0, 0, 1, 1] [1, 1, 1, 1, 0, 1, 1, 1, 0, 0] [5] BACKGROUND Assuming we’re given parallel corpuses, identify biased sentences. From there, identify biased articles. We assume that we’ve already given annotated corpuses to play with. In the future and in practice, we won’t have such a luxury. Data borrowed from earlier study [1] : Parallel topics: 2008 political atmosphere Drawn from political blog posts. Match sentences/articles with three types of bias: liberal, neutral, or conservative. PROBLEM STATEMENT Three categories, baseline accuracy is 33.33% (assuming completely random). Using simple bag-of-words approach we achieved ~40% accuracy for three-way classification. Using bag of words approach for binary classification, we achieve ~60% accuracy. CONCLUSION Bias != subjectivity Eg, “The second amendment protects gun owners’ rights in America,” versus “I think Inception was quite overrated.” More advanced bias detection techniques Smarter “dictionary” of very biased topics Ie, creating a list of words that give their sentences added priority when weighing sentiment Summing sentiment by topic, etc. FUTURE WORKS Some existing research directed towards related areas: Lexical bias indicators [1] Quantification of media bias using conservative and liberal blogs [2] Automated annotation [3] Detection of other linguistic constructs, such as sarcasm [4] However, no existing research directly tackling automated bias detection. Borrowed data (with permission) from “Shedding light… on biased language” Variety of NLP suites already popular, eg, NLTK Consulted with Professor Eric K. Meyer, of the UIUC College of Media. Topic framing, eg, estate tax vs. death tax. Choice of source, such as cherry- picked quotes and data points. Choice of words, eg, fetus vs. unborn baby. APPROACH Chart 1. Sentiment-Bias correlation analysis; shows little/no correlation Richard Lee, James Chen, Jason Cho Chart 2. Bias-Sentiment with word count analysis; sentiment has little impact Liberal vs. Neutral Cons. vs. Neutral Liberal & Cons. vs. Neutral Liberal vs. Cons. vs. Neutral Error39%39.9%39%61.3% Table1. Errors from classifier; bag-of-words experiences difficulties during textual classifications due to the high amount of overlapping features present in all datasets RESULTS, CONT. RESULTS