Personalized News Recommendation Using Twitter

Jonnalagedda, Nirmal; Gauch, Susan

doi:10.1109/wi-iat.2013.144

Cited by 51 publications

(29 citation statements)

References 12 publications

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“…Our interest was in certain key interest groups, not to identify every single group. Using specific user information (profile, tags, or comment based) to identify user groups sharing a common denominator (often a common interest, topics, emotions, or health conditions) is a shared element in news recommendations (see also Jonnalagedda and Gauch, 2013;Li et al, 2010) and e-health initiatives involving personalised health profiling (see Batool and Khan, 2012). Another approach to analysing data is to look at individual influencers.…”

Section: Limitations Of Exploratory Studymentioning

confidence: 99%

Security information sharing via Twitter: 'Heartbleed' as a case study

Jeske

McNeill

Coventry

et al. 2017

IJWBC

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Abstract:The current paper outlines an exploratory case study in which we examined the extent to which specific communities of Twitter users engaged with the debate about the security threat known as 'Heartbleed' in the first few days after this threat was exposed. The case study explored which professional groups appeared to lead the debate about Heartbleed, the nature of the communication (tweets and retweets), and evidence about behaviour change. Using keywords from the Twitter user profiles, six occupational groups were identified, each of which were likely to have a direct interest in learning about Heartbleed (including legal, financial, entrepreneurial, press, and IT professionals). The groups participated to different degrees in the debate about Heartbleed. This exploratory case study provides an insight into information sharing, potential communities of influence, and points for future research in the absence of a voice of authority in the field of cybersecurity.Keywords: Heartbleed; tweet content; influence; behavioural change. Pam Briggs holds a Chair in Applied Psychology (PaCT Lab, Northumbria University), delivering innovative research and consultancy around issues of identity, trust and security in new social media. Her research interests include understanding personal information disclosure, trust in electronic media, and online privacy.

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Section: Limitations Of Exploratory Studymentioning

confidence: 99%

Security information sharing via Twitter: 'Heartbleed' as a case study

Jeske

McNeill

Coventry

et al. 2017

IJWBC

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“…Lu and Lee developed a model that produced hashtag recommendations [25]. Jonnalagedda and Gaucher designed a hybrid application which identified the popular news based on commonly used words on Twitter, and from among these words which recommended the words that complied with the profile data self-defined by the user [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

Sevli

Küçüksille

2017

Teh. vjesn.

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Preporučeni sustav oglašavanja zasnovan na dinamičkoj analizi podataka turskih korisnika Twittera Original scientific paper Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user's profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback. Keywords: advertising recommendation; content filtering; map reduce; social network marketing Preporučeni sustav oglašavanja zasnovan na dinamičkoj analizi podataka turskih korisnika TwitteraIzvorni znanstveni članak Online okruženja, a posebno društvene mreže postala su snažna alternative objavljivanju oglasa. Za učinkovito oglašavanje važno je da se sadržaj poistovjećuje s očekivanjima ciljane publike. Uzimajući u obzir da se očekivanja mogu s vremenom promijeniti, potrebno je u realnom vremenu i dinamički prepoznati orijentaciju korisnika. U ovom su se radu u realnom vremenu analizirale poruke turskih korisnika Twittera i identificirala njihova trenutna očekivanja. U tu je svrhu dizajnirana web usluga koja analizira profil korisnika i daje oglase koji najbolje odgovaraju očekivanjima. Za filtriranje odgovarajućeg sadržaja oglašavanja korištena je metoda nazvana heuristička metoda odstranjivanja suvišnog (Heuristic Pruning Method -HPM). Razvijeni sustav je testiran na grupi volontera, aktivnih korisnika Twittera, a učinkovitost sustava se pokazala dobivenom povratnom informacijomfeedbackom.

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“…is calculated as follows. Recall that visited location may have more than one category: = getBinaryDataSet( , , ); (8) train binary classifier using data set ; (9) calculate accuracy , of classifier on validation data set; (10) if > Acc then (11) A c c = ; (12) = ; (13) end ( Considering independence assumption between previous visited categories we can write (2) as…”

Section: Considering Previous Visited Place and Timementioning

confidence: 99%

“…For example, Sadilek et al [7] used tweets to recommend those restaurants that user should not go. References [8][9][10] proposed a recommender system for news recommendations by modeling the user profile and exploiting the tweet-news relationship.…”

Section: Related Workmentioning

confidence: 99%

Twitter Can Predict Your Next Place of Visit

Chauhan

Tejwani

Toshniwal

2016

International Journal of Distributed Sensor Networks

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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%.

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Personalized News Recommendation Using Twitter

Cited by 51 publications

References 12 publications

Security information sharing via Twitter: 'Heartbleed' as a case study

Security information sharing via Twitter: 'Heartbleed' as a case study

Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

Twitter Can Predict Your Next Place of Visit

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