The Experts below are selected from a list of 5757 Experts worldwide ranked by ideXlab platform
David Jurgens - One of the best experts on this subject based on the ideXlab platform.
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that s what friends are for inferring location in online social media platforms based on social relationships
International Conference on Weblogs and Social Media, 2013Co-Authors: David JurgensAbstract:Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals' locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter Message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms.
Nigel Collier - One of the best experts on this subject based on the ideXlab platform.
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bidirectional lstm for named entity recognition in Twitter Messages
International Conference on Computational Linguistics, 2016Co-Authors: Nut Limsopatham, Nigel CollierAbstract:In this paper, we present our approach for named entity recognition in Twitter Messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter Message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks.
Nut Limsopatham - One of the best experts on this subject based on the ideXlab platform.
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bidirectional lstm for named entity recognition in Twitter Messages
International Conference on Computational Linguistics, 2016Co-Authors: Nut Limsopatham, Nigel CollierAbstract:In this paper, we present our approach for named entity recognition in Twitter Messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter Message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks.
Chandra Sekhar Vorugunti - One of the best experts on this subject based on the ideXlab platform.
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a novel online social network Twitter Message tweet classifier based on Message diffusion in the network
Communication Systems and Networks, 2017Co-Authors: S Jyothi, Chandra Sekhar VoruguntiAbstract:Online social Message classification is an important task for E-Commerce companies to mine and classify the customer opinions. In this paper, we have proposed a first of its kind of an efficient Message classification algorithm which is independent of tweet content and considers the set of followers who will retweet during the retweet peaks. By including the followers who will retweet during retweet peaks will get a better sampling of the followers set and reduces the computation and storage complexities drastically. Also, we have eliminated the heavy weight operations like DTW to perform the comparison task between the test vector and training vector. The preliminary experiment results authorize that the proposed system attains an accuracy of 95.96% in classification of tweet Messages.
Wenlin Liu - One of the best experts on this subject based on the ideXlab platform.
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Twitter Message types health beliefs and vaccine attitudes during the 2015 measles outbreak in california
American Journal of Infection Control, 2019Co-Authors: Cui Zhang Meadows, Lu Tang, Wenlin LiuAbstract:Background Social media not only provide platforms for the public to obtain information about a disease but also allow them to share their opinions and experiences about it. Methods This study analyzed 3000 tweets systematically selected from over 1 million tweets posted during the 2015 California measles outbreak. Results News updates were the most tweeted Messages (41.4%), followed by personal opinions (33.7%), resources (19.4%), personal experiences (2.5%), and questions (1.6%). Susceptibility was the most discussed health belief (21.8%), followed by cues to action (18.9%) and severity (13.0%). Individuals were significantly more likely to discuss severity. Nonprofit organizations were significantly more likely to offer cues to action than other user types, and media were less likely to include cues to action than other user types. Pro-vaccine tweets were more likely to contain links to traditional mainstream media sources such as newspapers and magazines, and anti-vaccine tweets were more likely to link to emerging news websites. Conclusions Understanding who posts what on social media during an infectious disease outbreak allows public health agencies to better assess the public's attitudes, sentiments, and needs in order to provide timely and effective information.
