Recommending targeted strangers from whom to solicit information on social media

Mahmud, Jalal; Zhou, Michelle X.; Megiddo, Nimrod; Nichols, Jeffrey; Drews, Clemens

doi:10.1145/2449396.2449403

Cited by 50 publications

(37 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jalal et al [150] built a crowd-source 'qCrowd' which automatically selects strangers in Twitter to get response for the information requested, proving both recommendation algorithm and prediction model. Twitter is used to analyze the real time traffic-related events.…”

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

View full text Add to dashboard Cite

Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors.

show abstract

Section: International Journal Of Applied Information Systems (Ijais)mentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

View full text Add to dashboard Cite

show abstract

“…For deriving personality traits Linguist Inquiry and Word Count (LIWC) [20] had been used frequently. References [20][21][22][23][24][25][26][27][28][29] have shown that lexicons used by people can be used for understanding their personal values and how to use these traits for a recommendation. Though all these approaches have been used extensively in analyzing personality traits, these also have shortcomings of predefined word category correlation.…”

Section: Related Workmentioning

confidence: 99%

Twitter Can Predict Your Next Place of Visit

Chauhan

Tejwani

Toshniwal

2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The present work focuses on predicting users' next place of visit using their past tweets. We hypothesize that tweets of the person have predictive power on his location and therefore can be used to predict his next place of visit. This problem is important for location based advertising and recommender based services. To predict the next place of visit, we calculate the probabilities of visiting different types of places using bank of binary classifiers and Markov models. More specifically, we train bank of binary classifiers on past tweets and calculated the probabilities of visiting next places. Since bank of binary classifiers is based on a bagof-words model, to account for time of last visited place and place itself, we built Markov models for different time duration to calculate probabilities of visiting next place. Empirical evaluation shows that by combining the probabilities obtained from bank of binary classifiers and Markov models the accuracy of predicting next place increased from 65% to 80%.

show abstract

“…In this perspective, a common approach early developed in community Q&A services [24,22] and in other popular social networks, such as Facebook and Twitter [17,25], consists in routing the questions to a list of the top-k appropriate users. The users' appropriateness is generally estimated using a set of features from the questions, the users themselves and their relations with the questioner.…”

Section: Introductionmentioning

confidence: 99%

Answering Twitter Questions

Soulier

Tamine

Nguyen

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18794The contribution was presented at CIKM 2016 :http://cikm2016.cs.iupui.edu/ ABSTRACTIn this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely "informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.

show abstract

Recommending targeted strangers from whom to solicit information on social media

Cited by 50 publications

References 26 publications

An Overview on User Profiling in Online Social Networks

An Overview on User Profiling in Online Social Networks

Twitter Can Predict Your Next Place of Visit

Answering Twitter Questions

Contact Info

Product

Resources

About