Twitris: A System for Collective Social Intelligence

Sheth, Amit P.; Jadhav, Ashutosh; Kapanipathi, Pavan; Chen, Lu; Purohit, Hemant; Smith, Gary A.; Wang, Wenbo

doi:10.1007/978-1-4939-7131-2_345

Cited by 15 publications

(19 citation statements)

References 21 publications

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“…We have explored a predictive analysis paradigm that comprises two levels of prediction, using coarse-grained analysis built upon fine-grained analysis. Such analysis have been conducted with creditable success for events such as elections, gun violence, drug misuse or illicit drug use [3].…”

Section: Resultsmentioning

confidence: 99%

“…During the 2016 US Presidential elections where swing states played a key role in determining the outcome, many polling agencies failed to predict it accurately 67 . On the other hand, researchers 8 conducted a real-time predictive analysis using a social media analytics platform [3], making the prediction accurately before the official outcome was announced, by analyzing the state-level signals, such as from Florida and Ohio. Temporal aspect was also important in this use case to explain the evolution of the public opinion based on milestone events over the period of the election, as well as the election day because people tend to express, who they voted in the same day.…”

Section: Us 2016 Presidential Electionmentioning

confidence: 99%

“…Gaining insights and improving situational awareness on issues that matter to the public are challenging tasks, and social media can be harnessed for a better understanding of the pulse of the populace. Accordingly, state-of-theart applications, such as Twitris [3] and OSoMe [2], have been developed to process and analyze big social media data in real time. Regarding availability and popularity, Twitter data is more common than data from web forums and Reddit 1 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive Analysis on Twitter: Techniques and Applications

Kursuncu

Gaur

Lokala

et al. 2018

Lecture Notes in Social Networks

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Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present 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 relate a few success stories.

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Section: Resultsmentioning

confidence: 99%

Section: Us 2016 Presidential Electionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Analysis on Twitter: Techniques and Applications

Kursuncu

Gaur

Lokala

et al. 2018

Lecture Notes in Social Networks

Self Cite

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“…We first collected data from Twitter for six disaster events from the last six years, using the keyword-based crawling approach. We collected tweets during hurricane Harvey in 2017 and Louisiana floods in 2016 using CitizenHelper system [26] and for prior events, re-used datasets available from previous works [27], [28]. Following the recommendation of collecting "contextual streams" [29], we further extended each event collection with messages that belonged to conversation chain (a Reply message thread on Twitter), where a conversation chain contained at least one message from an event dataset.…”

Section: A Collecting Conversation Streamsmentioning

confidence: 99%

Social-EOC: Serviceability Model to Rank Social Media Requests for Emergency Operation Centers

Purohit

Castillo

Imran

et al. 2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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The public expects a prompt response from emergency services to address requests for help posted on social media. However, the information overload of social media experienced by these organizations, coupled with their limited human resources, challenges them to timely identify and prioritize critical requests. This is particularly acute in crisis situations where any delay may have a severe impact on the effectiveness of the response. While social media has been extensively studied during crises, there is limited work on formally characterizing serviceable help requests and automatically prioritizing them for a timely response. In this paper, we present a formal model of serviceability called Social-EOC (Social Emergency Operations Center), which describes the elements of a serviceable message posted in social media that can be expressed as a request. We also describe a system for the discovery and ranking of highly serviceable requests, based on the proposed serviceability model. We validate the model for emergency services, by performing an evaluation based on real-world data from six crises, with ground truth provided by emergency management practitioners. Our experiments demonstrate that features based on the serviceability model improve the performance of discovering and ranking (nDCG up to 25%) service requests over different baselines. In the light of these experiments, the application of the serviceability model could reduce the cognitive load on emergency operation center personnel, in filtering and ranking public requests at scale.

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“…Example applications include news reporting [4,16], disaster coordination [9], law enforcement [2], health emergencies such as infectious disease outbreaks [13], and prediction of election or referendum outcomes [6]. In situations like these, where access to timely information is key, an ability to process social media in a real-time fashion becomes an important requirement [15,17]. This presents new challenging issues for the research community in order to quickly make sense of torrential social streams as they come out, and to make the most from the fresh knowledge available from these streams.…”

Section: Introductionmentioning

confidence: 99%