A Network-Aware Approach for Searching As-You-Type in Social Media

Lagrée, Paul; Cautis, Bogdan; Vahabi, Hossein

doi:10.1145/2806416.2806435

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…These ongoing trends exploit social search behaviors (Lagre et al , 2015) and sentiments (Liu, 2005) as cues for consumers and sellers’ decisions (Dhar and Simonson, 2003)[1]. In fact, thanks to the availability of the aggregated frequency of search queries[2] from some search engines services, such as those provided by Google Trends[3], these new kinds of search services, also known as query of search queries[4], become their own new social media the content of which, just trends of search, is created by users.…”

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

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“…We extend in this article a preliminary study published in [Lagrée et al 2015], introducing the following novel contributions, which allow us to give a complete picture on our algorithmic solutions:…”

Section: Comparison With Previous Publicationmentioning

confidence: 99%

As-You-Type Social Aware Search

Lagrée

Cautis

Vahabi

2017

ACM Trans. Intell. Syst. Technol.

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Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, i.e., performing a search with a given prefix, and showing the top ranked results. In this paper we focus on as-you-type keyword search over social media, that is data published by users who are interconnected through a social network. We adopt a "network-aware" interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in micro-blogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.

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A Network-Aware Approach for Searching As-You-Type in Social Media

Lagrée

Cautis

Vahabi

2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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We present in this paper a novel approach for as-you-type top-k keyword search over social media. We adopt a natural "networkaware" interpretation for information relevance, by which information produced by users who are closer to the seeker is considered more relevant. In practice, this query model poses new challenges for effectiveness and efficiency in online search, even when a complete query is given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and classic IR indexes such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate it through extensive experiments for several applications and search scenarios, including searching for posts in micro-blogging (Twitter and Tumblr), as well as searching for businesses based on reviews in Yelp. They show that our solution is effective in answering realtime as-you-type searches over social media.

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A Network-Aware Approach for Searching As-You-Type in Social Media

Cited by 3 publications

References 31 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

As-You-Type Social Aware Search

A Network-Aware Approach for Searching As-You-Type in Social Media

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