Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Hate Speech Detection with Comment Embeddings

Abstract: We address the problem of hate speech detection in online user comments. Hate speech, defined as an "abusive speech targeting specific group characteristics, such as ethnicity, religion, or gender", is an important problem plaguing websites that allow users to leave feedback, having a negative impact on their online business and overall user experience. We propose to learn distributed low-dimensional representations of comments using recently proposed neural language models, that can then be fed as inputs to a classification algorithm. Our approach addresses issues of high-dimensionality and sparsity that impact the current state-of-the-art, resulting in highly efficient and effective hate speech detectors.

No free lunch in data privacy

Abstract: Differential privacy is a powerful tool for providing privacy-preserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers changes very little with the addition or deletion of any tuple. It is frequently accompanied by popularized claims that it provides privacy without any assumptions about the data and that it protects against attackers who know all but one record. In this paper we critically analyze the privacy protections offered by differential privacy.First, we use a no-free-lunch theorem, which defines non-privacy as a game, to argue that it is not possible to provide privacy and utility without making assumptions about how the data are generated. Then we explain where assumptions are needed. We argue that privacy of an individual is preserved when it is possible to limit the inference of an attacker about the participation of the individual in the data generating process. This is different from limiting the inference about the presence of a tuple (for example, Bob's participation in a social network may cause edges to form between pairs of his friends, so that it affects more than just the tuple labeled as "Bob"). The definition of evidence of participation, in turn, depends on how the data are generated -- this is how assumptions enter the picture. We explain these ideas using examples from social network research as well as tabular data for which deterministic statistics have been previously released. In both cases the notion of participation varies, the use of differential privacy can lead to privacy breaches, and differential privacy does not always adequately limit inference about participation.

Predicting consumer behavior with Web search

Abstract: Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

Effect of iodine intake on thyroid diseases in China.

Abstract: Background Iodine is an essential component of thyroid hormones; either low or high intake may lead to thyroid disease. We observed an increase in the prevalence of overt hypothyroidism, subclinical hypothyroidism, and autoimmune thyroiditis with increasing iodine intake in China in cohorts from three regions with different levels of iodine intake: mildly deficient (median urinary iodine excretion, 84 μg per liter), more than adequate (median, 243 μg per liter), and excessive (median, 651 μg per liter). Participants enrolled in a baseline study in 1999, and during the five-year follow-up through 2004, we examined the effect of regional differences in iodine intake on the incidence of thyroid disease. Methods Of the 3761 unselected subjects who were enrolled at baseline, 3018 (80.2 percent) participated in this follow-up study. Levels of thyroid hormones and thyroid autoantibodies in serum, and iodine in urine, were measured and B-mode ultrasonography of the thyroid was performed at baseline and follow-up. Results Among subjects with mildly deficient iodine intake, those with more than adequate intake, and those with excessive intake, the cumulative incidence of overt hypothyroidism was 0.2 percent, 0.5 percent, and 0.3 percent, respectively; that of subclinical hypothyroidism, 0.2 percent, 2.6 percent, and 2.9 percent, respectively; and that of autoimmune thyroiditis, 0.2 percent, 1.0 percent, and 1.3 percent, respectively. Among subjects with euthyroidism and antithyroid antibodies at baseline, the five-year incidence of elevated serum thyrotropin levels was greater among those with more than adequate or excessive iodine intake than among those with mildly deficient iodine intake. A baseline serum thyrotropin level of 1.0 to 1.9 mIU per liter was associated with the lowest subsequent incidence of abnormal thyroid function. Conclusions More than adequate or excessive iodine intake may lead to hypothyroidism and autoimmune thyroiditis.

Unjust waters: climate change, flooding and the urban poor in Africa

Abstract: Many of the urban poor in Africa face growing problems of severe flooding. Increased storm frequency and intensity related to climate change are exacerbated by such local factors as the growing occupation of floodplains, increased runoff from hard surfaces, inadequate waste management and silted-up drainage. One can distinguish four types of flooding in urban areas: localized flooding due to inadequate drainage; flooding from small streams within the built-up area; flooding from major rivers; and coastal flooding. ActionAid undertook participatory vulnerability analysis in five African cities, to explore local people's perceptions of why floods occur, how they adjust to them, who is responsible for reducing the flood risk and what action the community itself can take. While local people adapt to floods, recognition of local, national and international governments' and organizations' responsibility to act to alleviate flooding and its causes, especially the consequences of climate change, is urgently needed.