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On-line Trend Analysis with Topic Models: \#twitter Trends Detection Topic Model Online

Jey Han Lau, Nigel Collier, Timothy Baldwin
2012 International Conference on Computational Linguistics  
We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.  ...  Our topic model has an in-built update mechanism based on time slices and implements a dynamic vocabulary.  ...  Acknowledgements NICTA is funded by the Australian government as represented by Department of Broadband, Communication and Digital Economy, and the Australian Research Council through the ICT centre of  ... 
dblp:conf/coling/LauCB12 fatcat:y3i3u5ocx5e6zdqi7fdirnkvte

Dynamic hyperparameter optimization for bayesian topical trend analysis

Tomonari Masada, Daiji Fukagawa, Atsuhiro Takasu, Tsuyoshi Hamada, Yuichiro Shibata, Kiyoshi Oguri
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
This paper presents a new Bayesian topical trend analysis.  ...  We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm.  ...  Dynamic Topic Models (DTM) [3] and its continuous time version (cDTM) [13] utilize transitions of the parameters of per-topic word multinomials for modeling document temporality, where the vectors  ... 
doi:10.1145/1645953.1646242 dblp:conf/cikm/MasadaFTHSO09 fatcat:4zgmx3vfg5h5xe2xjokir5gdaq

Recruitment Market Trend Analysis with Sequential Latent Variable Models

Chen Zhu, Hengshu Zhu, Hui Xiong, Pengliang Ding, Fang Xie
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
However, traditional methods for recruitment market analysis largely rely on the knowledge of domain experts and classic statistical models, which are usually too general to model large-scale dynamic recruitment  ...  Recruitment market analysis provides valuable understanding of industry-specific economic growth and plays an important role for both employers and job seekers.  ...  For modeling the trend of recruitment market, we first divide all job postings into different data units {χe,t}e,t with respect to companies and timestamps, which contain job postings of company e at epoch  ... 
doi:10.1145/2939672.2939689 dblp:conf/kdd/ZhuZXDX16 fatcat:rqlcic3envb7pnjeusa575q5xe

A non-parametric mixture model for topic modeling over time [article]

Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing
2012 arXiv   pre-print
In this paper we propose non-parametric Topics over Time (npTOT), a model for time-varying topics that allows an unbounded number of topics and exible distribution over the temporal variations in those  ...  A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally expensive inference methods.  ...  Qualitative analysis To see how npTOT can capture a wider variety of temporal variation than TOT, consider topics found using both models.  ... 
arXiv:1208.4411v1 fatcat:ektt7t3pwng37iui5l47tcghgy

A nonparametric mixture model for topic modeling over time [chapter]

Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
In this paper we propose nonparametric Topics over Time (npTOT), a model for time-varying topics that allows an unbounded number of topics and flexible distribution over the temporal variations in those  ...  A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally expensive inference methods.  ...  Qualitative analysis To see how npTOT can capture a wider variety of temporal variation than TOT, consider topics found using both models.  ... 
doi:10.1137/1.9781611972832.59 dblp:conf/sdm/DubeyHWX13 fatcat:65qhb6yup5emzbmtddzs2ytthm

Cross-Domain Analysis of the Blogosphere for Trend Prediction [chapter]

Patrick Siehndel, Fabian Abel, Ernesto Diaz-Aviles, Nicola Henze, Daniel Krause
2012 Lecture Notes in Social Networks  
Due to their high popularity they are a valuable source of information regarding public opinions about all kind of topics.  ...  to predict the monetary success of movies and music with high accuracy.  ...  [9] formalize the notion of long-running chatter topics consisting recursively of spike topics generated by outside world events and show that there is a correlation between the number of posts related  ... 
doi:10.1007/978-3-7091-1346-2_12 dblp:series/lnsn/SiehndelADHK13 fatcat:omel6wwxzvasnalfqooyi3ne6i

ThemeDelta: Dynamic Segmentations over Temporal Topic Models

Samah Gad, Waqas Javed, Sohaib Ghani, Niklas Elmqvist, Tom Ewing, Keith N. Hampton, Naren Ramakrishnan
2015 IEEE Transactions on Visualization and Computer Graphics  
We present ThemeDelta, a visual analytics system for extracting and visualizing temporal trends, clustering, and reorganization in time-indexed textual datasets.  ...  ThemeDelta is supported by a dynamic temporal segmentation algorithm that integrates with topic modeling algorithms to identify change points where significant shifts in topics occur.  ...  Thus, in their model, topics generate both observed timestamps and words.  ... 
doi:10.1109/tvcg.2014.2388208 pmid:26357213 fatcat:upw26szkmbe3hclqotfmtngrjm

Modeling the evolution of associated data

Jie Tang, Jing Zhang
2010 Data & Knowledge Engineering  
To model topic distributions, we associate to each topic a continuous distribution over time, so that topics are responsible for generating both observed words and timestamps.  ...  Many real-world applications, for example topic detection and tracking (TDT), research trend analysis on scientific papers, and hot topic finding from newsgroup posts need to consider the evolution of  ...  The NEWSGROUP data shows stronger temporal patterns. Many of the topics found have sharply shaped trends. Some topics (e.g., Topic #4 and Topic #6) from NEWSGROUP are very sensitive to the time.  ... 
doi:10.1016/j.datak.2010.03.009 fatcat:zjiafor2kncjhkpmplni7be2ye

Evolving Networks and Social Network Analysis Methods and Techniques [chapter]

Mário Cordeiro, Rui P. Sarmento, Pavel Brazdil, João Gama
2018 Social Media and Journalism - Trends, Connections, Implications  
are now required to scale and deal with the temporal dimension in case of streaming settings.  ...  ., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks.  ...  Acknowledgements This work was fully financed by the Faculty of Engineering of the Porto University.  ... 
doi:10.5772/intechopen.79041 fatcat:x4m2g5borjfijknqck54ierppq

What are developers talking about? An analysis of topics and trends in Stack Overflow

Anton Barua, Stephen W. Thomas, Ahmed E. Hassan
2012 Empirical Software Engineering  
Such knowledge repositories can be invaluable for gaining insight into the use of specific technologies and the trends of developer discussions.  ...  Our analysis allows us to make a number of interesting observations, including: the topics of interest to developers range widely from jobs to version control systems to C# syntax; questions in some topics  ...  Topic Trends Over Time (RQ3) We also wish to analyze the temporal trends of topics.  ... 
doi:10.1007/s10664-012-9231-y fatcat:xrwuwmlurfcafewoubbzooo4oi

Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model [article]

Toktam A. Oghaz, Ece C. Mutlu, Jasser Jasser, Niloofar Yousefi, Ivan Garibay
2020 arXiv   pre-print
Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data.  ...  Online social media platforms are turning into the prime source of news and narratives about worldwide events.  ...  To achieve probabilistic modeling of narratives over topical trends, we incorporate the components of narratives including named-entities and temporal-causal coherence between events into our topical model  ... 
arXiv:2004.06793v1 fatcat:37bqkesjorba5n7b4cxg72dyzi

Modeling Topical Trends over Continuous Time with Priors [chapter]

Tomonari Masada, Daiji Fukagawa, Atsuhiro Takasu, Yuichiro Shibata, Kiyoshi Oguri
2010 Lecture Notes in Computer Science  
We model topical trends by per-topic Beta distributions as in Topics over Time (TOT), proposed as an extension of latent Dirichlet allocation (LDA).  ...  In this paper, we propose a new method for topical trend analysis.  ...  In this paper, we focus on the applications of multitopic probabilistic models like LDA [5] to topical trend analysis.  ... 
doi:10.1007/978-3-642-13318-3_38 fatcat:sixqn6etfbafnjgg4tu6n36jim

A Geometry-Driven Longitudal Topic Model

Yu Wang, Conrad Hougen, Brandon Oselio, Walter Dempsey, Alfred Hero
2021 Harvard data science review  
A simple and scalable framework for longitudinal analysis of Twitter data is developed that combines latent topic models with computational geometric methods.  ...  Practical application of the proposed framework is demonstrated through its ability to capture and effectively visualize natural progression of latent COVID-19-related topics learned from Twitter data.  ...  All authors provided critical feedback and helped shape the research, analysis and manuscript.  ... 
doi:10.1162/99608f92.b447c07e doaj:23143f3dd7e449d1be6552936c5d8e55 fatcat:vmvz4j3ax5eohp2hc2pg6kzteq

Detecting Topics and Sentiments of Public Concerns on COVID-19 Vaccines with Social Media Trend Analysis (Preprint)

Michal Monselise, Chia-Hsuan Chang, Gustavo Ferreira, Rita Yang, Christopher C. Yang
2021 Journal of Medical Internet Research  
Topic modeling resulted in 50 topics of those we selected the 12 topics with the highest volume of tweets for analysis.  ...  Using the combination of topic detection and sentiment analysis, we identify different types of concerns regarding vaccines that are expressed by different groups of the public that appear in social media  ...  Any opinions and conclusions or recommendations expressed in this study are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.2196/30765 pmid:34581682 pmcid:PMC8534488 fatcat:44jzcdfr25h5poecu2bphkcqqu

A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [article]

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
Fake news detection is one of the most analyzed and prominent areas of research. These detection techniques apply popular machine learning and deep learning algorithms.  ...  Fake news and misinformation are a matter of concern for people around the globe. Users of the internet and social media sites encounter content with false information much frequently.  ...  The UI allows users to upload a screengrab of a tweet from which the model extracts useful information like tweet text, image, username, timestamp, location, etc., and predict the authenticity of a tweet  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4
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