It is now an evident trend that automated survey using social media as sources complements and will eventually largely replace manual surveys. That is an unstoppable direction as social media are becoming the major outlets of public opinions. The technology is ready too. Automated survey, or auto poll, refers to the use of computers to collect the public opinions and sentiments on a topic. The data sources are social media big data where people are discussing most every topic all the time. The technology is a parser that reads social media posts and mines salient information (facts, evaluations and emotions) about any topic. More specially, deep information extraction and sentiment analysis are the required and mature text mining technology that can be enabled by an underlying parser. This is the part of Artificial Intelligence that is proven to work and has been serve the clients in the business world (e.g. our customer insight products). Polls can provide quantitative information for decision-making in government, businesses and the general public, enjoying an extremely wide range of applications for many years. The presidential election is a prominent example, polls are conducted from time to time during the election to inform the voters as well as the president candidates how the public feels about the race so voters can make an educated choice and the candidate president teams can adjust their policies and campaign strategy to enhance their public image. Product launch is an example of the enterprise, feedback collected from customer surveys can help businesses to detect issues and to address them. Auto-poll is dong the same, just that it is doing it much faster, more comprehensive, in a larger scale and is less costly. Compared with the traditional manual questionnaires or polls, auto-poll has the following salient features. Real time. No need to go through a series of traditional survey process, designing the questionnaire, distributing them or by telephone interviews or street interviews, collecting and summarizing the results, with all steps carried out manually. It often takes days or even weeks to complete a serious survey. But auto-polls are instant, you get results as soon as you enter your topic. As long as there are people discussing it , the insights will mined out of the text sea. For any topic, using automated survey is as easy as using a search engine with the same response time but much more accurate results, Our deep parser reads social media day and night to feed our storage just as a search engine indexes the Internet in their storage. Low cost . Manual surveys are constantly struggling between the required costs and the scale of surveys (bigger scale reduces the error margin to be more reliable and convincing). They often have to compromise the sample size given the budget. Auto-poll is done fully automatically by the system, and the same system can serve a variety of different customers in different topics, each poll is inexpensive, costing just a fraction of the traditional poll. The sample size can easily be magnitudes higher than that of manual surveys (often millions of data points vs. several thousand data points), way beyond the reach of most traditional polls. Objectivity . Traditional polls or surveys need to design a questionnaire, which may intentionally or unintentionally introduce subjective bias or implied suggestions. Auto poll is bottom-up data analysis and mining, hence more objective by nature. The public opinions are collected from the natural comments people make on topics, not as a response to a designed specific question. Moreover, in order to collect sufficient number of survey responses, the investigators administering the surveys sometimes need to offer incentives, which introduce a possible bias because some customers who answer the surveys too quickly to be honest, mainly do so to gain rewards, not to really air their opinions, causing the return of low-quality or polluted results. Multi-topic comparison . This is particularly important, because almost for any topic, we need a competitor or industry as background to figure out the real image in public mind. For example, the poll on Obama's presidential campaign's effectiveness is of little sense if it is not contrasted to his rival Romney. Likewise, customer surveys on ATT's cellular network service is inseparable from comparisons of its competitors like Verizon. Ideally, a full picture will be clear on one brand once it is in comparison with all leading brands in the same industry. In theory, manual surveys can also perform multi-brand comparisons, but in practice, the costs and time required to investigate many brands at the same time are often beyond feasible. Investigators have had to reduce or sacrifice on the front of investigation of competitors, and use the limited resources on their own brand. Automated survey is different, multi-topic survey and comparison of these products is designed as a feature in these systems, it is just as easy as surveying one brand in this fully automated environment. BPI (Brand Passion Index) in our products is one such feature that instantly surveys multiple brands in one industry and compar them in three dimensions, buzz (size of the bubble), popularity (up or down in the graph), passion intensity (right or left in the graph: the more right, the more intense). For example, the illustration of BPI for the US retail stores gives a clear picture of the landscape and where each brand stands in its space.. In short, we are entering a big data age with no short of information on any topics you may need to study. With mobile-web and social media in everyone's hands, public opinions and sentiments are buried in the big data calling for deep technology to mine. Thus, there is absolutely no doubt that automated survey will become the direction of polls as the mainstream. Its supporting technologies are mature, large-scale multi-lingual text mining system that parses and reads big data around the world is just around the corner. Related posts in my original Chinese blog: 【立委科普：自动民调】 奥巴马赢了昨晚辩论吗？舆情自动检测告诉你 社会媒体舆情自动分析：马英九 vs 陈水扁 舆情自动分析表明，谷歌的社会评价度高出百度一倍 【置顶：立委科学网博客NLP博文一览（定期更新版）】
点击 On line listening This is the VOA Special English Health Report. Old age may not sound exciting. But recent findings offer good news for older people and for people worried about getting older. Researchers found that people become happier and experience less worry after they reach the age of fifty. In fact, they say by the age of eighty-five, people are happier with their life than they were when they were eighteen years old. The findings came from a survey of more than three hundred forty thousand adults in the United States. The Gallup Organization questioned them by telephone in two thousand eight. At that time, the people were between the ages of eighteen and eighty-five. The researchers asked questions about emotions like happiness, sadness and worry. They also asked about mental or emotional stress. Arthur Stone in the Department of Psychiatry and Behavioral Science at Stony Brook University in New York led the study. His team found that levels of stress were highest among adults between the ages of twenty-two and twenty-five. The findings showed that stress levels dropped sharply after people reached their fifties. Happiness was highest among the youngest adults and those in their early seventies. The people least likely to report feeling negative emotions were those in their seventies and eighties. The study also showed that men and women have similar emotional patterns as they grow older. However, women at all ages reported more sadness, stress and worry than men. The findings appeared in the Proceedings of the National Academy of Sciences. Researchers say they do not know why happiness increases as people get older. One theory is that, as people grow older, they grow more thankful for what they have and have better control of their emotions. They also spend less time thinking about bad experiences. Professor Stone says the emotional patterns could be linked to changes in how people see the world, or maybe even changes in brain chemistry. The researchers also considered possible influences like having young children, being unemployed or being single. But they found that influences like these did not affect the levels of happiness and well-being related to age. And that's the VOA Special English Health Report, written by Brianna Blake. Tell us what you think about the relationship between happiness and age. You can post comments on our website, http://www.hxen.com , or on Facebook and Twitter at VOA Learning English. I'm Barbara Klein. 音频下载： 20110824b.mp3 同步字幕： 20110824b.lrc
Papers on the Community Structure Topics : A recent survey of Fortunato (2010) Finding statistically significant communities in networks, A. Lancichinetti, F. Radicchi, J.J. Ramasco and S. Fortunato Overlapping Communities in Dynamic Networks: Their Detection and Mobile Applications, N. P. Nguyen, T. N. Dinh, S. Tokala, and M. T. Thai, (Mobicom 2011) FacetNet: A Framework for Analyzing Communities and Their Evolutions in Dynamic Networks, Y.-R. Lin, Y. Chi, S. Zhu, H. Sundaram, and B. L. Tseng, (WWW 2008) Graphscope: A parameter-free mining of large time-evolving graphs, J. Sun, C. Faloutsos, S. Papadimitriou, and P. S. Yu, (KDD 2007) Community Structure in Time-Dependent, Multiscale, and Multiplex Networks, P. J. Mucha, T. Richardson, K. Macon, M. A. Porter, and J.-P. Onnela, (Science 2010) Discovering Network Structure Beyond Communities, T. Nishikawa and A. E. Motter, (Nature 2011) Algorithm for parametric community detection in networks, A. Bettinelli, P. Hansen, and L. Liberti, (Physical Review E, 2012) A markov random walk under constraint for discovering overlapping communities in complex networks, D. Yin, B. Yang, C. Baquero, D. Liu, D. He, and J. Liu, (J. STATE MECH 2011) Community Discovery and Profiling with Social Messages, W. Zhou, H. Jin, and Y. Liu, (KDD 2012) Vertex neighborhoods, low conductance cuts, and good seeds for local community methods, D. Gleich and C. Seshadhri (KDD 2012) Community Detection in Dynamic Social Networks: A Random Walk Approach, L.-C. Huang, T.-J. Yen, and S. T. Chou (ASONAM 2011) Community Detection in Incomplete Information Networks, W. Lin et. al (WWW 2012) Using Content and Interactions for Discovering Communities in Social Networks, M. Sachan, D. Contractor, T. Faruquie, and L. V. Subramaniam (WWW 2012)
IEEE PES Member Survey 问卷节选 The Institute of Electrical and Electronics Engineers ( IEEE , read I-Triple-E) 的 Power Energy Society ( PES ) 的Member Survey 问卷节选： 20. Outside of IEEE, please indicate which groups, associations or societies that you belong to (1) American Nuclear Society (2) American Society Mechanical Engineers (3) American Society of Civil Engineers (4) American Public Power Association (5) American Solar Energy Society (6) American Wind Energy Association (7) Association of Energy Engineers (8) CIGRE (9) CIRED (10) Edison Electric Institute (11) Engineers without Borders (12) Institution of Engineering and Technology (13) National Rural Electric Cooperative Association (14) National Society of Professional Engineers (15) National Hydropower Association (16) Society of Hispanic Professional Engineers (17) Society of Industrial Engineering (18) Society of Manufacturing Engineers (19) Society of Women Engineers (20) Solar Electric Power Association (21) Society of Industrial Engineering (22) None 23. Which publications do you read regularly? Please indicate as many as apply? (1) Energy Biz (2) Electric Energy (3) Electricity Today (4) Electric Light Power (5) Electrical Source (6) Intelligent Utility (7) Power Energy Power Grid International (8) Power Electronics Technology (9) Public Utilities (10) Rural Electric (11) Transmission Distribution World (12) Underground Construction (13) Other (please specify) 24. Which industry web sites do you visit on a regular basis? Please select as many as apply. Which industry web sites do you visit on a regular basis? Please select as many as apply. (1) www.awea.org (2) www.doe.gov (3) www.eei.org (4) www.electricity-today.com (5) www.electricenergyonline.com (6) www.elp.com (7) www.energycentral.com (8) www.ieee-pes.org (9) www.ieee.org (10) www.fortnightly.com (11) www.nreca.coop (12) www.sepa.org (13) www.seia.org (14) www.tdworld.com (15) Other (please specify) 25. What methods of communication do you use for your professional activities? Check 1 for the most frequent and check 3 for the least frequent. Please make one selection per row. (1) Web sites (2) E-Newsletters (3) E-magazines (4) Blogs (5) Paper Magazines (6) Paper Newsletters (7) Facebook (8) Twitter (9) Linked In (10) Webinars
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Google in China Nature survey Dear xu peiyang, Nature magazine is conducting a survey of Chinese scientists to assess how much they have come to rely on Google for their work. Recent news stories highlight the fact that Google may pull out of China if it is not allowed to provide uncensored search results in the country. Since you are a prominent scientist working in China, Nature would like to know what impact losing access to Google and its related products would have on your research. The survey is completely anonymous, and includes ten questions designed to find out what field you work in, and how you use Google and/or other search engines. Please click on this link to fill out the online survey. It should take no more than two minutes, but could help to provide the scientific community with vital information about how online search engines have become a key tool in research in China. The survey is hosted by an online survey website called Survey Monkey, which is a legitimate website. To take the survey, please go to: http://www.surveymk.com/s/RV3ZMLB The results of the survey will be analysed by Nature staff, and will hopefully be used in a forthcoming article. If you have already received an invitation to complete this survey, we thank you for your participation and request that you ignore this email. Many thanks for your assistance! On behalf of Nature, Sara Grimme
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