Twitris: A System for Collective Social Intelligence

  title={Twitris: A System for Collective Social Intelligence},
  author={A. Sheth and Ashutosh Jadhav and Pavan Kapanipathi and Chen Lu and Hemant Purohit and Gary Alan Smith and Wenbo Wang},
  booktitle={Encyclopedia of Social Network Analysis and Mining},
Citizen Sensing Humans or citizens on the ubiquitous Web, acting as sensors and sharing their observations and views using mobile devices, mobile apps, and Web 2.0 services CitizenSensor Network An interconnected network of people who actively observe, report, collect, coordinate, analyze, disseminate, and act upon information via text, links to other resources, and various media including audio, images, and videos PeopleContentNetwork Analysis (PCNA) Social media analytics takes into account… 

Social media analytics and internet of things: survey

The literature review indicates that there are fewer research works done in the area of social media analytics and IoT compared to Data Mining and IoT, and this paper facilitates discussion and elicits research potentials in social media Analytics and IoT integration.

A Unified Semantic Model for Cross-Media Events Analysis in Online Social Networks

A unified semantic model for events analysis is proposed that contains well-designed classes and properties to tackle the lack of unified representation, and provenance information is also taken into consideration.

What's my age?: Predicting Twitter User's Age using Influential Friend Network and DBpedia

This work proposes to create a machine learning system coupled with the DBpedia graph that predicts the most probable age of the Twitter user, and explores the existing state of the art approaches.

Twitter vigilance: A multi-user platform for cross-domain Twitter data analytics, NLP and sentiment analysis

  • D. CenniP. NesiG. PantaleoImad Zaza
  • Computer Science
    2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
  • 2017
Twitter Vigilance has been designed as a cross-domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users' influence network, Natural Language Processing and Sentiment Analysis of textual content.

Generic architecture of a social media-driven intervention support system for smart cities

This paper proposes a novel approach of creating an Intervention Support System (ISS) interface for public services of a city to easily and effectively monitor attitude trends of public for topics of interest, while hiding all the complex functionality of collecting, processing, and mining big user-generated data from social media.

CitizenHelper-Adaptive: Expert-Augmented Streaming Analytics System for Emergency Services and Humanitarian Organizations

  • Rahul PandeyHemant Purohit
  • Computer Science
    2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
  • 2018
An interactive user-feedback based streaming analytics system ‘CitizenHelper-Adaptive’ to mine social media, news, and other public Web data streams for emergency services and humanitarian organizations and actively learns to improve the models for efficient information processing and organization is presented.

Predictive Analysis on Twitter: Techniques and Applications

This chapter presents fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse- grained analysis of Twitter data for making decisions and taking actions, and relates a few success stories.

Web Textual Processing Feature Extraction Machine Learning Health Public Health Political Issues Social Issues Transportation Disaster Management Community on Social Media Demographics Anomaly

This chapter discusses techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data, and presents fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse- grained analysis ofTwitter data for making decisions and taking actions.

Building the Multi-Modal Storytelling of Urban Emergency Events Based on Crowdsensing of Social Media Analytics

This paper proposes a novel urban emergency event storytelling method to generate multi-modal summary from Weibo, and conducts extensive case studies on real-world microblog datasets to demonstrate the superiority of the proposed framework.

EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services

This paper describes the design and implementation of an open-source technology-based learning analytics system ‘​EMAssistant’ for the emergency volunteers or practitioners referred as the trainee, to enhance their experiential learning cycle with the cause-effect reasoning on providing relevant feedback to the machine learning model.



Citizen sensor data mining, social media analytics and development centric web applications

This tutorial will focus on social intelligence applications for social development, and cover the following research efforts in sufficient depth: 1) understanding and analysis of informal text, esp. microblogs, and 2) building social media analytics platforms.

Twitris v3: From Citizen Sensing to Analysis, Coordination and Action

This demonstration will show Twitris’ comprehensive capabilities in spatio-temporal-thematic, people-contentnetwork, and sentiment-emotion-subjectivity analyses, with examples from business intelligence including brand tracking and advertising campaigns, social/political unrests, and disaster events such as U.S. Election 2012, Occupy Wall Street (OWS) protest, Hurricane Sandy, etc.

Intent Classification of Short-Text on Social Media

This paper addresses the problem of multiclass classification of intent with a use-case of social data generated during crisis events by exploiting a hybrid feature representation created by combining top-down processing using knowledge-guided patterns with bottom-up processing using a bag-of-tokens model.

Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter

This study performs exploratory analysis on Twitter to understand the dynamics of user engagement by studying what attracts a user to participate in discussions on a topic, and identifies various factors which might affect user engagement, ranging from content properties, network topology to user characteristics on the social network.

User Interests Identification on Twitter Using a Hierarchical Knowledge Base

It is argued that the hierarchical semantics of concepts can enhance existing systems to personalize or recommend items based on a varied level of conceptual abstractness and be relevant to a user’s interests.

Multimodal social intelligence in a real-time dashboard system

A variety of technologies to implement near real-time data analytics to transform Social Intelligence into Business Intelligence and evaluate their effectiveness in the music domain are discussed.

Cursing in English on twitter

This paper examines the characteristics of cursing activity on a popular social media platform - Twitter - involving the analysis of about 51 million tweets and about 14 million users to explore a set of questions that have been recognized as crucial for understanding cursing in offline communications.

Harnessing Twitter "Big Data" for Automatic Emotion Identification

The experiments demonstrate that a combination of unigrams, big rams, sentiment/emotion-bearing words, and parts-of-speech information is most effective for gleaning emotions.

On Understanding the Divergence of Online Social Group Discussion

The approach allows to systematically study collective diverging group behavior independent of group formation design and can help to prioritize whom to engage with in communities for specific topics of needs during disaster response coordination, and for specific concerns and advocacy in the brand management.

Prediction of Topic Volume on Twitter

It is found that combining features from multiple aspects (especially past activity information and network features) yields the best performance, and that leveraging more past information leads to better prediction performance, although the marginal benefit is diminishing.