Twitris: A System for Collective Social Intelligence
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- 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
- Citizen-Sensor 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
- People-Content-Network Analysis (PCNA)
Social media analytics takes into account social media users (People), data shared on social media websites (Content), and the network of social media users (Network)
- Semantic Web
Semantic Web is a group of methods and technologies to help machines and humans understand the meaning – or “semantics” – of data on the World Wide Web
- Sentiment-Emotion-Intent (SEI) Extraction
Analyzing social media content to extract insights about social media users’ sentiment (positive, negative, and neutral), emotion (happy, angry, upset, etc.), and the user’s intention (seeking information, sharing information, etc.)
- Social Media Analytics
The practice of gathering data from social media websites and analyzing that data to gain new insights and facilitate informed decisions and actions
- Spatio-Temporal-Thematic (STT) Analysis
Social media analytics takes into account what is being said about an event (theme) and where (spatial) and when (temporal) it is being said
The massive amount of data on social networks (e.g., Twitter, Reddit, Facebook, Instagram, Web forums) provides an exceptional opportunity for leveraging citizen sensing (user expressed observations and opinions) for collective intelligence (deeper insights that drive decisions and actions based on social data) in numerous domains – retailing including branding and marketing, financial markets, entertainment and sports, disaster coordination, social movements, healthcare and epidemiology, etc. However, social data is extremely rich, providing opportunity to understand them from many dimensions such as spatio-temporal-thematic, people-content-network, and sentiment-emotion-intention. It takes a rich variety of semantic techniques including knowledge graphs or ontology enhanced or empowered text mining, natural language processing, and machine learning (including deep learning). Twitris is an embodiment of all these for real-time and highly scalable technology used both in scientific research and in commercial applications.
More than two third of all Internet users have become “citizens” of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. With over five billion mobile connections, of which a majority (over 2.5 billion) use smartphones, a significant portion of the humanity has the ability to communicate using SMS, and digital media can be shared with the rest of the humanity instantly. As a result, humanity is interconnected as never before. This interconnected network of people who actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing (Sheth 2009a, b). This phenomenon is different from the traditional centralized information dissemination and consumption environments where citizens primarily act as consumers of reported information from several authoritative sources.
This citizen sensing is complemented by the growing ability to access, integrate, dissect, and analyze individual and collective thinking of humanity, giving us a capability that is recognized as collective intelligence. Citizen sensing involves humans in the loop and with it all the complexities associated with and intelligence captured in human communication. As citizen sensing has gained momentum, it is generating millions of observations and creating significant information overload. In many cases, it becomes nearly impossible to make sense of the information around a topic of interest. Given this data deluge, analyzing the numerous social signals can be extremely challenging. In response to this growing citizen sensing data deluge, Twitris has been developed with the vision of performing semantics-empowered analysis of a broad variety of social media exchanges.
Twitris v3: Sentiment-emotion-intent (SEI) extraction with multidimensional user and content modeling (Chen et al. 2012a; Wang et al. 2012; Nagarajan et al. 2009b; Purohit et al. 2013b, 2014a, 2015) along with personalization (Kapanipathi et al. 2011a) and emerging continuous semantics (Sheth et al. 2010) capability involving semantic social stream (i.e., real-time) processing using dynamically generated and updated domain models for semantics and context
Following the above research phases, Twitris went through a substantial system re-architecture and software engineering work with emphasis on user interface/user experience, usability, scalability, and robustness, leading to a version termed Twitris-C. This version has since been licensed to spin off a startup, Cognovi Labs, in 2016.
The above versions, or phases, of Twitris’ development are not as granular as painted above – that is, the issues identified above are not explicitly segregated by the version of the Twitris which has been in continuous development with senior students graduating and new students picking up the work. Around a dozen of talks, including tutorials, cover many of the issues covered by Twitris (Sheth 2009a, 2011; Nagarajan 2010; Nagarajan et al. 2011; Purohit et al. 2013c).
Event-specific analysis of citizen sensing and discussion of opportunities and challenges in understanding temporal, spatial, and thematic cues.
Facets of people-content-network analysis with focus on user-community engagement analysis.
Real-time social media data analysis and the concept of continuous semantics supported by dynamic model creation.
Sentiment, emotion, and intent identification from citizen sensing data.
Recent advances in developing semantic abstracts or semantic perception to convert massive amounts of raw observational data into nuggets of information and insights that can aid in human decision-making and real-time decision-making.
The idea of research leading to Twitris occurred on November 26, 2008. Terrorists struck Mumbai, India, and over the next three days, they proceeded to make mayhem in nine locations. Each of the nine subevents of this overall event separated by time and location (space) had distinct thematic elements or topical content. The importance of Twitter, especially in terms of citizen sensing – the ability of a regular person to use his or her mobile device to share his or her personal observation, thoughts, and beliefs, well before the traditional news media has a chance to do reporting and to shape opinions – was extensively discussed in the immediate aftermath of this momentous event. This event also gave us a clear case for the needs and benefits of analyzing social media content such as tweets and Flickr posts and related news stories along the three dimensions of spatial (location of observation), where; temporal (time of observation), when; and thematic (the event in question), what (Battle 2009; Impact Lab 2008).
The Twitris Platform and Three Research Stages of Its Evolution
Twitris v1: Spatio-Temporal-Thematic (STT) Processing of Twitter and Associated News, Multimedia, and Wikipedia Content
Data collection: collect user posted tweets pertaining to an event from Twitter, associated news, multimedia, and Wikipedia content (Fig. 2).
Data analysis: (a) process obtained tweets to extract strong event descriptors considering spatial, temporal, and thematic event attributes and (b) process event related news, multimedia, and Wikipedia content to get event context and gain a better understanding.
Visualization: present extracted summaries on Twitris v1 user interface.
Twitris v2: People-Content-Network Analysis (PCNA) with Use of Background Knowledge and Semantic Metadata Extraction and Querying/Exploration
The Mumbai terrorism event of 2008 gave the impetus to study the event from STT dimensions and to focus on connecting with relevant news content. Social media continues to grow and revolutionize the way users interact with each other and information. Social network users are not only creators and recipients of the information but also critical relays to propagate information. This powerful ability of sharing has played an important role in events with varied social significance, audience, and duration, such as political movements (e.g., the Jasmine Revolution in Tunisia), brand management and marketing, and, perhaps most visibly, crisis and disaster management (e.g., Haitian and Japanese earthquakes). The Twitris team started to look at the issues such as the role of content nature for high vs. low attributed information diffusion (a phenomenon of propagating messages via friendship/follower connections among users of social network) (Nagarajan et al. 2010) and user engagement (given a discussion topic surrounding an event on social media, what motivates a user to engage in the discussion for his/her first and subsequent interaction across the various phases of the event) (Purohit et al. 2011, 2014c; Ruan et al. 2012). Consequently, Twitris v2 embarked on a more comprehensive analysis along the three pillars of what makes anything social: who is engaging in the social activity, what is being communicated, and how does this communication flow between those engaged in the social activity. The idea is to gain insights into how permanent and transient networks arise and what and why information flows across such networks. Twitris v2 developed the significant capability to extract more types of metadata, and the infrastructure became more semantic with the use of Semantic Web standard RDF as well as relevant background knowledge. The latter enabled Twitris v2 to support the deep exploration capability with use of DBpedia and SPARQL over metadata extracted from the tweets. Twitris v2 research that focused on coordination during disasters also led to integrating Twitris with Ushahidi’s SwiftRiver open source platform and support ingestion of SMS which were used for events such as Pakistan floods in 2010.
Evolving ad hoc nature of social media communities:
Event-centric communities with varied nature (Purohit et al. 2011) often bring together users from different parts of the social network, especially in Twitter where we keep switching discussions of our interests, and we may not already be connected to other participants of those communities. Therefore, in such ad hoc communities, it is difficult to depend on just follower graphs for understanding the dynamics. Twitris v2 introduced analysis of user interaction networks so that human dynamics in the evolving communities can be understood at granular levels – influencer analysis, contextually important people with roles to engage with, community evolution, etc. Twitris v2 built this feature by extending our research in the user interaction network analysis on brand-page communities (Purohit et al. 2012) and crisis response coordination for identifying important actors in social media communities (Purohit et al. 2014b).
Contrast in the structure of interaction networks:
Figure 7 shows the networks of influencers in two topical communities during the Occupy Wall Street (OWS) movement, “Occupy Chicago” on the left and “Occupy LA” on the right. Such an analysis provides insights to understand not only the real dynamics of the actors (e.g., what organizations supporters belong to and to whom are they strongly connected) but also the potential of the influencers to drive actions in the communities (tightly connected influencers are likely to drive effective “call for action” propagation in the communities). In this figure, the influencer network of Occupy LA is highly connected and self-organized as compared to sparsely connected one for Occupy Chicago and, therefore, likely to reach masses effectively for any call for action. Even the Facebook page for Occupy LA reflected such activism (Fig. 7).
Slicing and dicing the networks by user features:
To glean insights about actionable information in the ad hoc communities, we need to understand the participants better. Therefore, Twitris v2 introduced slicing and dicing analysis of the interaction networks by providing user/node centric features. For example, the professional or organizational affiliation of users provides clues to understand the cause for dynamics – e.g., who are the people behind the organized network of Occupy LA? Are such users from the same type of organizations led to coordinated actions? Similarly, Twitris v2 introduced the content-centric analysis, thus realizing the full potential of PCNA. Users are clustered by grouping them into sentiment segments of the target topic, thus answering questions like which candidate is going stronger in the influencer network from a sentiment perspective (Fig. 8) between Mitt Romney and Ron Paul and for what issues?
Understanding group dynamics by community evolution:
Twitris v2 focused on the larger goal of predictive ability for group dynamics (Purohit et al. 2014c), and the people-content-network analysis (PCNA) framework was the key to the untapped potential of group dynamics. Therefore, Twitris v2 created clusters in the ad hoc communities based on the sentiment of the users for a targeted topic over time and associated events on the timeline for causal analytics. Figure 9 shows an example of community evolution centered around Republican presidential nominee Mitt Romney during March 1–31, 2012. It shows three snapshots taken over a 10-day period, and we observed an extremely modularized community in the end of the analysis, which was not really the case for the closest competition, Rick Santorum. And as we know, Santorum exited the race on April 9. Thus, the analysis of community evolution made Twitris v2 capable of understanding group dynamics of ad hoc communities by not limiting the output to just understand users but also the group behavior.
Twitris v3: Emotion-Sentiment-Intent, Real-Time View, and Other Advancements
Behind every (well, most of the important) tweet, there is a human. And a human is complex. Through a tweet, a person expresses emotion, sentiment, and intent. Understanding this dimension is a key to unlock the true potential of social media. This is especially true for monetization of social media. Understanding an underlying intent can tell us if a user is expressing a transactional (potentially for buying a product) intent, seeking information, or just sharing information (Nagarajan et al. 2009b). Sentiment is perhaps the most sought after type of analysis of social data. Currently, it is the primary basis of social media analysis to predict whether a product or a movie will succeed, who is more likely to win an election, or to attempt to identify consumer interest and hence use it for targeting the advertisement. Analysis of or identification of emotion is likely the dark horse of the three – while techniques for its analysis are not yet as mature as sentiment analysis, it is likely to be combined with the other two to give far more signal than without it.
A key innovation in sentiment analysis, employed in Twitris v3, is topic-specific sentiment analysis – to associate sentiment with an entity (Chen et al. 2012a; Chen 2016). This enables us to identify two different sentiments associated with different entities in a single tweet. For example, in the tweet “The King’s Speech was bloody brilliant. Colin Firth and Geoffrey Rush were fantastic!” we can identify both the sentiment (i.e., bloody brilliant) associated with the movie “The King’s Speech” and the sentiment (fantastic) associated with the actors Colin Firth and Geoffrey Rush. More recently, we are associating sentiments with events – when there is a significant change in sentiment, we attempt to associate that with real-world events. For example, by tracking both the event- and entity-specific sentiments, Twitris v3 is able to capture a substantial increase of positive sentiment toward President Obama on the immigration issue on June 15, 2012 (the day on which President Obama outlined a new immigration policy), and associate it with the event descriptors such as “dream act,” “obama’s immigration move,” and “new immigration policy.” Figure 10 shows that Twitter users have the opposite sentiments toward two candidates: Obama (green/positive) and Romney (red/negative) on the same topic “final debate.” The reason is that Obama received more positive feedback from Twitter users than Romney did, which is in line with the impression from news media. This example demonstrates Twitris’ power in identifying topic-specific sentiments.
Beyond sentiment and emotion, there is an intentional behavior expressed in the social media posts, such as intent of asking help during a disaster response. The Twitris research project led to design of intent mining techniques, especially for a context of disaster response where mining intent of seeking and offering help can greatly assist response operations for coordination. Identifying intent of a post can be formulated as a text classification problem (Purohit et al. 2015), although it is a different type of problem concerned with the future state of affairs, in contrast to topic classification–focused on subject matter of the post, as well as sentiment and emotion classification – focused on the current state of affairs. For instance, in a message “RT @xyz: Again, people in #yeg feeling helpless about #yycflood and wanting to help, go donate blood. Clinics in #yyc are closed.?!!!,” topic classification focuses on the medical resource “blood”; sentiment and emotion classification is focused on the negative feeling expressed via “helpless.” In contrast, intent classification concerns the author’s intended future action, i.e., “wanting to help/donate.” We developed techniques (Purohit et al. 2013b, 2014a, 2015) for intent classification using a hybrid feature representation created by a combination of top-down processing based knowledge-guided patterns and bottom-up processing based bag-of-tokens model. Pattern-aided text-classification was found to perform well on the well-formatted text and, therefore, shows potential to improve intent-based text classification for short-text of social media. We employed diverse patterns from a variety of knowledge sources including declarative patterns from domain experts, syntactic-semantic patterns from psycholinguistics and discourse analysis theories about conversations, and contrast-mining-based patterns to tackle the sparsity challenge for intent classification.
We also explored the commercial intent problem domain for how to automatically identify users’ intents from posts so that monetization can be more targeted on users’ needs (Nagarajan et al. 2009b). The highlight of our study is that we discover and differentiate three types of posts: (a) transactional posts, e.g., “I am looking for a 32 GB iTouch”; (b) information sharing posts, e.g., “I like my new 32 GB iTouch”; and (c) information seeking posts, e.g., “what do you think about 32 GB iTouch?” For monetization purposes, transactional posts and information seeking posts are more valuable than information sharing posts because users are looking for information that advertisers can exploit. By extracting intent/keywords/cues from transactional and information seeking posts, our system achieved an accuracy of 52% on ad impressions using MySpace and Facebook data, while the baseline, without using our system, only achieved an accuracy of 30%.
Detailed research on social data analysis encompasses social intelligence in real time (Gruhl et al. 2010), which involved a Kno.e.sis-IBM collaboration leading to the operationally deployed BBC Sound Index system. In addition, Twitris has been used for research on multiple fronts for understanding social behavior in online communities, including prediction of topic volume on Twitter (Ruan et al. 2012), brand tracking (Purohit et al. 2012), psycholinguistic analysis during emerging coordination for actionable intentional behaviors of help (Purohit et al. 2013a, b, 2014a, 2015), privacy-aware content dissemination (Kapanipathi et al. 2011b) and personalization through user interest modeling (Kapanipathi et al. 2014), user-community engagement (Purohit et al. 2011, 2014c), information diffusion (Nagarajan et al. 2010), trust in social media (Thirunarayan and Anantharam 2011), studying election events (Chen et al. 2012b), analyzing cursing behavior on social media (Wang et al. 2014), and monetization of social activities (Nagarajan et al. 2009b) reported in over 40 publications and summarized in comprehensive tutorials (Nagarajan et al. 2011; Purohit et al. 2013c).
Twitris-C: Commercialization and Further Advances
Information sourcing pipeline and aggregation framework: while Twitris originally started with a focus on analysis of Twitter, it has since been expanded to incorporate or source and aggregate data from multiple sources. This include a JSON API that accepts data, typically from proprietary, privacy controlled or enterprise sources. Examples include feedback and product review data, as well as data from Facebook. Another important source of data is a version of Twitris handles is Web forums. Modules to incorporate Reddit and Instagram are expected shortly.
- Twitris has been engineered with the understanding that all components can and will be updated. To this end, we have implemented a modularized architecture where possible.
Twitris’ real-time processing system uses Apache Storm which is inherently modular in its use of “bolts.” These bolts are self contained processing units. To add a new form of analysis, a filter or a new database connection, all one has to do is add a bolt.
Additionally, Twitris’ processing pipeline allows for custom plugins to be integrated into the system. Plugins can be written in a number of different programming languages and can be turned on or off, made available for specific campaigns or campaign types, and assigned custom output fields via the Twitris UI.
- Web Services
Twitris uses the Django web-framework for all interaction with processed data. Django allows developers to write custom apps to interact with Twitris’ data in new and interesting ways.
- Front End
- UI/UX enhancements for both campaign designer and end user (those using campaigns for getting insights and making decisions),
- Campaign creation and editing in real-time.
Widgets on the management page aid the campaign designer in monitoring the effectiveness of the campaign
- Ability to generate If This Then That (IFTTT classifiers for incoming data)
In addition, the ability to use the output of custom plugins in the classifier
- Widget to search Twitter and import the last week’s worth of data
Useful for supplementing streaming data or bootstrapping a new campaign
Sophisticated text search can be used in combination with faceted search simultaneously to provide full query control.
Web app is scalable from mobile to desktop.
Scalability (Twitris runs on a large Open Stack cluster with 864 CPUs, 17TB main memory, 18TB SSD, and 435TB disk space)
- Real-time monitoring of all its components, leading to improved resource allocation, optimization capabilities, and recovery
- A combination of Nagios, Grafana, Graphite, and StatsD allow System Administrators to:
Monitor the status of each system through web dashboards
- Receive email alerts for
Resource over utilization
Identify systems that are under or over utilizing resources
Using Apache JMeter allows us to test the system while we monitor the stress on the system
Illustrative Real-World Applications
Twitris has been used in a research context for studying and analyzing social sensing and perception of a broad variety of events: politics and elections, social movements and uprisings, crisis and disasters, epidemiology and epidemic tracking, environment, etc. Its commercialization by Cognovi Labs has allowed it to pursue more commercial applications including brand tracking and advertising campaign effectiveness, sports and entertainment, defense and intelligence, and empowering professional users. We present several real-world applications. Some of the applications, especially those focused on disaster response and popular events of year 2016 – Brexit and US Presidential Election, involve real-time analysis and its use for outcome prediction.
Twitris-C was licensed to create a startup, Cognovi Labs in 2016. The technology was used for a series of significant successes. Specifically, we were able to predict Brexit hours before the polls closed in the UK and before the US markets closed (Donovan 2016; Sheth 2016a), and it was used to correctly predict 2016 US elections, well before anyone else did (Cognovi Labs 2016; Sheth 2016b).
Measuring public attitude, providing timely analysis for public engagement and policy making on important social issues: this was exemplified by the use case of gender-based violence (GBV) (Purohit et al. 2016b).
Using social media for understanding brand development (Yuksel et al. 2016). In this case, Twitris was used over Facebook data related to the brand under study.
- Social media analysis for epidemiological surveillance. Examples of such uses include
Studying prescription drug abuse associated with opioid dependence (Daniulaityte et al. 2015.
Study of conversations on Zika related to disease characteristics: symptoms, transmission, prevention, and treatment (Miller et al. 2017.
Analyzing community level health and disease challenges: this was exemplified by analyzing clinical depressive symptoms in Twitter (Yazdavar et al. 2016).
Use of Twitris for supporting personalized digital health, with use case of asthma in children is online.
In the near term, Twitris and its continued enhancement aim to (a) rapidly create campaign-specific knowledge graphs (background knowledge) using a knowledge graph creation tool and use the knowledge graph in enhanced semantic processing (Sheth and Thirunarayan 2012), (b) enhance subjectivity analysis, (c) study time and location correlated issues across data from multiple heterogeneous data streams, (d) carry out more studies that combine social media analysis with more traditional data collection (e.g., surveys), and (e) enhance the ability to carry out more behavioral economics applications. In the medium term, campaigns will include not only social media content but also streaming data from sensors (Internet of Things).
We acknowledge contributions of these alumni and team members whose work has benefitted Twitris in different ways: Karthik Gomadam, Meena Nagarajan, and Ajith Ranabahu, and Pramod Anantharam, Shreyansh Bhatt, Prof. Krishnaprasad Thirunarayan, and Prof. Valerie Shalin. This work was partially supported by these NSF funded grants: “SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response” (IIS1111182), “I-Corps: Towards Commercialization of Twitris – a system for collective intelligence,” (1343041), and “PFI:AIR – TT: Market Driven Innovations and Scaling up of Twitris – A System for Collective Social Intelligence” (1542911). It is also partially supported by these NIH grants: “Modeling Social Behavior for Healthcare Utilization in Depression” (1 R01 MH105384-01A1) and “Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use” (5R01DA039454-02). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the investigator(s) and do not necessarily reflect the views of the sponsor.
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- Sheth A (2016b) Election day #SocialMedia analysis #Election2016 [LinkedIn article]. https://www.linkedin.com/pulse/election-day-socialmedia-analysis-election2016-06nov2016-amit-sheth?trk=prof-post. Accessed 25 Feb 2017
- Thirunarayan K, Anantharam P (2011) Trust networks: interpersonal, sensor, and social. In: Proceedings of 2011 international conference on collaborative technologies and systems (CTS 2011), Philadelphia, 23–27 May 2011Google Scholar
- Wang W, Chen L, Thirunarayan K, Sheth A (2012) Harnessing Twitter ‘Big data’ for automatic emotion identification. In: Proceedings of international conference on social computing (SocialCom), 2012, Amsterdam, 3–5 Sept 2012Google Scholar
- Wang W, Chen L, Thirunarayan K, Sheth AP (2014) Cursing in english on twitter. In: Proceedings of the 17th ACM conference on computer supported cooperative work & social computing, ACM, pp 415–425Google Scholar
- Yazdavar AH, Al-Olimat HS, Banerjee T, Thirunarayan K, Pathak J, Sheth A (2016) Analyzing clinical depressive symptoms in Twitter. Paper presented at 23rd NIMH conference on mental health services research (MHSR): harnessing science to strengthen the public health impact, BethesdaGoogle Scholar
- Yuksel K, Biggemann S, Sheth A, Brunn J (2016) Using social media data to understand brand development. 2016 Direct/interactive marketing research summit (EDGE16), Los Angeles, 15 Oct 2016Google Scholar
- Sheth A, Thirunarayan K (2012) Semantics empowered Web 3.0: managing enterprise, social, sensor, and cloud-based data and services for advanced applications. Morgan & Claypool. ISBN: 1608457168Google Scholar