By Adam Wierzbicki, Ulrik Brandes, Frank Schweitzer, Dino Pedreschi
This booklet constitutes the refereed court cases of the twelfth foreign convention and college of community technology, NetSci-X 2016, held in Wroclaw, Poland, in January 2016.
The 12 complete and six brief papers have been rigorously reviewed and chosen from fifty nine submissions. The papers take care of the research of community types in domain names starting from biology and physics to laptop technological know-how, from monetary markets to cultural integration, and from social media to infectious diseases.
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Additional resources for Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings
Next, we selected the tweets that are posted between Aug 25, 2014 and Nov 25, 2014, discarding tweets that are not written in English, stop words in tweets, and users with less than 10 tweets. 2 million tweets. Next, we adopt T-LDA  to learn topics from these tweets. Zhao et al.  showed that T-LDA can uncover topics in tweets better than several other 20 W. Gong et al. Table 1. Topics and some related keywords. Topics Some related keywords Topics Arts Art, Artwork, @fineartamerica Adult Some related keywords Adult, Porn, Sex, Pornography Books Journal, Book, Poet, Writer, Author Business Business, Economy, Finance, Oil Cars F1, Formula, Driver, BMW, Car Deals Chance, Deals, Contest, Cashback Education Education, Library, Publish Food Food, Cook, Recipe, Restaurant Fitness, Health Fitness, Health, Workout, Gym, Muscle, Weight, Training, Treatment Fashion Fashion, #ootd, #nyfw, Carpet, Dress, Collection, Beauty, Style Gaming Game, Xbox, PS4, Gaming, Dota, Jokes, Funny, Joke, Humor, LOL, Humour, League Funny Fun Music Music, #mtvstars, Concert, kpop Quotes Quote, Happiness, Positive Personal activity Eating, Super, God, Hell, Moment, Feeling, Asleep, Weather Politics Politics, Obama, War, Immigration, Election, Congress, Minister, Military Religion Religion, Lord, Buddhism, Christain Sports Sports, Basketball, NBA, Football, Goal Technology, Science Technology, Tech, Google, Apple, Mobile, NASA, Science, Solar, Comet, Earth Twitter Twitter, Followers, Unfollowers, Fustunfollow, Unfollowed Travel Travel, Tour, Vacation, Hotel, Island TV & Films TV, Movie, Trailer, Plot, IMDb Video Video, Youtuber, Youbube, Viewer LDA based methods.
We use all the survey participants in reading topic discovery evaluation. Table 3 shows the performance of Received-Content, ReadPopularity and Followee-Expertise and their combined rankings. We summarize our observations as follows. First, all our ranking strategies perform signiﬁcantly better than Random. Secondly, compared with Read-Popularity and FolloweeExpertise, Received-Content does not predict user reading topics well. One possible reason is the diﬃculty of inferring topics in tweets. Another possible reason is that Twitter users are only interested in a subset of tweets they received.
Table 3. Performance (MAP@n) of reading topic discovery. 2 Lurkers Active Users Fig. 5. Performance of predicting lurkers and active users’ reading topics. groups. Secondly, overall, predicting active users’ reading topics is easier than predicting lurkers’. Thirdly, Read-Popularity does not perform well for lurkers. It shows that compared with active users, lurkers are less likely to pay attention to the popular reading topics. Lastly, we ﬁnd that Followee-Expertise performs best for the lurker group.
Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings by Adam Wierzbicki, Ulrik Brandes, Frank Schweitzer, Dino Pedreschi