A scalable approach to extracting mobility patterns from social media data

Zhang, Zhenhua; Wang, Shaowen; Cao, Guofeng; Padmanabhan, Anand; Wu, Kaichao

doi:10.1109/geoinformatics.2014.6950845

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Social media are more and more employed as indicators of public opinions of the real-world phenomena (Zhang et al , 2014; Gao et al , 2014; Xia et al , 2014), from epidemiology, that tries to predict the crash of pandemic diseases (Ginsberg et al , 2009), to economy, interested in how correlated are job-related queries with the rise and fall of unemployment rate. Furthermore, political science uses search query data to recognize patterns about the amount of political contributions raised by candidates.…”

Section: Toward the Near Future: Social Trend-based Decisionsmentioning

confidence: 99%

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

D'Avanzo¹,

Pilato²,

Lytras³

2017

PROG

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Purpose An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

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Section: Toward the Near Future: Social Trend-based Decisionsmentioning

confidence: 99%

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

D'Avanzo¹,

Pilato²,

Lytras³

2017

PROG

View full text Add to dashboard Cite

show abstract

Design of a Novel Query System for Social Network

Virmani

Juneja

Pillai

2019

Journal of Information Technology Research

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User intention and nature of network plays a vital role towards the quality of response received as the result of any user query. Therefore, the need of system understanding the user's intent and network dynamism as well is highly apparent. The proposed query processing and analysing system (QPAS) for social networks is based on extracting user's intent from various social networks using existing NLP techniques. It fetches the information and further employs hybrid ensemble k-means hierarchical agglomerative clustering (HEKHAC) and modified Bitonic sort to improve the responses. The proposed approach offers an edge over other mechanisms as it not only retrieves more user-centric results as compared to traditional way of keyword-based searching but also in timely manner as well. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a noteworthy revolution scoring up to precision and recall respectively.

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A scalable approach to extracting mobility patterns from social media data

Cited by 2 publications

References 12 publications

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

Design of a Novel Query System for Social Network

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