Finding local experts on twitter

Yang

Trans Emerging Tel Tech

et al. 2019

The opinions of local experts in the location‐based social network are of great significance to the collection and dissemination of local information. In this paper, we investigated in‐depth how the user comments can be used to identify the local expert over social networks. We first illustrate the existences of potential local experts in a social network using a scored model by considering the personal profiles, comments, friend relationship, and location preferences. Then, a multidimensional model is proposed to evaluate the local expert candidates and a local expert discovery algorithm is proposed to identify local experts. Meanwhile, a scoring algorithm is proposed to train the weights in the model. Finally, an expert recommendation list can be given based on the score ranks of the candidates. Experimental results demonstrate the effectiveness of proposed model and algorithms.

“…LocalRank: The topic and location authority comprehensive algorithm proposed in the work of Cheng et al The formula is as follows:

s ((),, v_{i}, q) = s_{l} (() l ((), v_{i}), l ((), q)) * s_{t} (() t ((), v_{i}), t ((), q)),

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Local experts finding using user comments in location‐based social networks

Yang

Trans Emerging Tel Tech

et al. 2019

Proceedings of the 17th International Conference on Electronic Commerce 2015

“…[5] proposed a multi-agent based algorithm to find related persons, topics and venues automatically from each network. [2] proposed a local expert finding on twitter. They used over 15 million geo-tagged data from Twitter.…”

Section: Related Work 21 Expert Findingmentioning

confidence: 99%

Design of Contextual Local Expert Support Mechanism

Liou

2015

Nowadays many smartphone users share their experiences, pictures, geo-data and other forms of information through their devices. Mobile crowd sourcing will gather important knowledge from the masses. Unfortunately, we had little knowledge about local information. Local expert service can search each crowd's knowledge and experience in the local area via social media. In this paper, we propose a contextual local expert finding mechanism to support the solving of the problem faced by users. Our system is implemented as a mobile app to find a list of local experts based on one's professional, social, and context information. Our experimental results show that the proposed mechanism outperforms other benchmark approaches.

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

“…Each element in the vector is associated with a expertise topic word tw (e.g., "technology"), and the element value indicates to what extent the candidate is an expert in the corresponding topic. As presented in our previous work [6], we define the Local Expert Finding problem as: DEFINITION 1. (Local Expert Finding) Given a query q that includes a query topic t(q), and a query location l(q), find the set of k candidates with the highest local expertise in query topic t(q) and location l(q).…”

Section: Localrank: Problem Statement and Solution Approachmentioning

confidence: 99%

“…Request permissions from permissions@acm.org. SIGIR'14, July [6][7][8][9][10][11]2014 you know any good, available web developers?). Indeed, a recent Yahoo!…”

Section: Introductionmentioning

confidence: 99%

Who is the barbecue king of texas?

Cheng

Caverlee

Barthwal

et al. 2014

Self Cite

This paper addresses the problem of identifying local experts in social media systems like Twitter. Local experts -in contrast to general topic experts -have specialized knowledge focused around a particular location, and are important for many applications including answering local information needs and interacting with community experts. And yet identifying these experts is difficult. Hence in this paper, we propose a geo-spatial-driven approach for identifying local experts that leverages the fine-grained GPS coordinates of millions of Twitter users. We propose a local expertise framework that integrates both users' topical expertise and their local authority. Concretely, we estimate a user's local authority via a novel spatial proximity expertise approach that leverages over 15 million geotagged Twitter lists. We estimate a user's topical expertise based on expertise propagation over 600 million geo-tagged social connections on Twitter. We evaluate the proposed approach across 56 queries coupled with over 11,000 individual judgments from Amazon Mechanical Turk. We find significant improvement over both general (non-local) expert approaches and comparable local expert finding approaches.