Real-Time Story Detection and Video Retrieval from Social Media Streams

2019 ◽  
pp. 17-52
Author(s):  
Lyndon Nixon ◽  
Daniel Fischl ◽  
Arno Scharl
Keyword(s):  
Social Media ◽  
Real Time ◽  
Video Retrieval ◽  
Story Detection

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

Social Listening to Create Bespoke Customer Experiences: Best Practices for Hospitality Operators

2021 ◽  
pp. 193896552199308
Author(s):  
Kathryn A. LaTour ◽  
Ana Brant
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Real Time ◽  
Brand Image ◽  
Customer Experience ◽  
Current Experience ◽  
Social Listening

Most hospitality operators use social media in their communications as a means to communicate brand image and provide information to customers. Our focus is on a two-way exchange whereby a customer’s social posting is reacted to in real-time by the provider to enhance the customer’s current experience. Using social media in this way is new, and the provider needs to carefully balance privacy and personalization. We describe the process by which the Dorchester Collection Customer Experience (CX) Team approached its social listening program and share lessons to identify best practices for hospitality operators wanting to delight their customers through insights gained from social listening.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Real-time clinician text feeds from electronic health records

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
James T. H. Teo ◽  
Vlad Dinu ◽  
William Bernal ◽  
Phil Davidson ◽  
Vitaliy Oliynyk ◽  
...  
Keyword(s):  
Social Media ◽  
Electronic Health Records ◽  
Real Time ◽  
Capacity Planning ◽  
Low Cost ◽  
Disease Outbreaks ◽  
Record System ◽  
Health Records ◽  
Keywords And Phrases ◽  
Electronic Health

AbstractAnalyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

Real-time context-aware social media recommendation

2018 ◽  
Vol 28 (2) ◽  
pp. 197-219 ◽  
Author(s):  
Xiangmin Zhou ◽  
Dong Qin ◽  
Lei Chen ◽  
Yanchun Zhang
Keyword(s):  
Social Media ◽  
Real Time ◽  
Context Aware

scholarly journals A Distributed Content-Based Video Retrieval System for Large Data-sets

2021 ◽  
Author(s):  
ElMehdi SAOUDI ◽  
Said Jai Andaloussi
Keyword(s):  
Real Time ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Data Set ◽  
Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

scholarly journals Crowdsourced Countersurveillance: A Countersurveillant Assemblage?

2018 ◽  
Vol 16 (1) ◽  
pp. 20-38 ◽  
Author(s):  
Mark Andrew Wood ◽  
Chrissy Thompson
Keyword(s):  
Social Media ◽  
Knowledge Discovery ◽  
Real Time ◽  
Camera Traps ◽  
Traffic Surveillance ◽  
Breath Testing ◽  
Data Flows ◽  
Police Presence ◽  
New Form ◽  
Use Of Knowledge

Speed camera ‘traps’, random breath testing (RBT) stations, and other forms of mobile traffic surveillance have long been circumvented by motorists. However, as technologies for traffic surveillance have developed, so too have technologies enabling individuals to monitor and countersurveil these measures. One of the most recent forms of these countersurveillance platforms can be found on Facebook, where dedicated regional and national RBT and ‘police presence’ pages publicly post the locations of various forms of police surveillance in real-time. In this article, we argue that Facebook RBT pages exemplify a new form of social media facilitated countersurveillance we term crowdsourced countersurveillance: the use of knowledge-discovery and management crowdsourcing to facilitate surveillance discovery, avoidance, and countersurveillance. Crowdsourced countersurveillance, we argue, represents a form of countersurveillant assemblage: an ensemble of individuals, technologies, and data flows that, more than the sum of their parts, function together to neutralize surveillance measures. Facilitated by affordances for crowdsourcing, aggregating, and crowdmapping geographical data information on surveillance actors, crowdsourced countersurveillance provides a means of generating ‘hybrid heterotopias’: mediated counter-sites that enable individuals to contest and circumvent surveilled spatial arrangements.

Sign in / Sign up

Export Citation Format

Share Document