[PDF] Top 20 Predicting and Characterising User Impact on Twitter

... We process the text in the tweets of D1 and com- pute daily unigram frequencies. By discarding terms that appear less than 100 times, we form a vocabulary of size |V | = 71, 555. We then form a user term-frequency ... See full document

... Figure 2 presents the MAE values for all algorithms (including our “aver- aging" method) when trained on different sets of features (TS, NS, PB) and in different time windows for every country. In the majority of the ... See full document

... The majority of research on extracting missing user attributes from social media profiles use costly hand-annotated labels for supervised learning. Distantly super- vised methods exist, although these gener- ally ... See full document

... We have collected a large number of Dutch Twit- ter messages (hundreds of millions) and showed how they can be used for predicting the results of the Dutch Senate elections of 2011. Counting the tweets that ... See full document

... and Twitter, exhibit some levels of homophily [1, ...on Twitter usually share common topical interests [22, ...infer user attributes such as gender and age, personality traits and sentiment [1, 21, ... See full document

... per user by learning a rich representation of the user’s ...of Twitter user demographics have correlated errors based on user behavior is considered in Wood-Doughty et ... See full document

... the Twitter timeline: the network structure (follower-following ratio, follower frequency, following frequency) and the com- munication behavior of individuals (response frequency, retweet frequency, tweet ... See full document

... important impact on NLP, but may also have a intense impact on management sciences, political science, economics, and social sciences as they are all affected by user ...micro-blogs, Twitter, ... See full document

... While user attribute extraction on social media has received considerable attention, existing approaches, mostly supervised, encounter great difficulty in obtaining gold standard data and are therefore limited to ... See full document

... Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation Michael Denkowski, Chris Dyer and Alon Lavie.. Predicting and Characterising User Impact on Twitte[r] ... See full document

... trends. Predicting categorical values is referred to as classification, but if the goal is to model values or continuous functions it is referred to as ...to Twitter users, they gathered data from a ... See full document

... of Twitter users, their respective job titles, posted textual content and platform-related ...new user at- tribute that can be embedded in a multitude of downstream ... See full document

... [8] Current cell phones models are all prepared to do constantly running information accumulation programming to total various sensor readings. These gadgets would thus be able to give a dedicated record of a client's ... See full document

... detect Twitter spam, made use of account and content features, such as account age, number of followers or followings, URL ratio, and the length of tweet to distinguish spammers and ...make Twitter as a ... See full document

... Twitter does not allow pornographic material in profile, header or background images, but many accounts ignore this rule. This disregard for the Terms of Service could arguably be reason enough to find and remove ... See full document

... With so much news being consumed online, there is great interest in the way this news is consumed – what articles do users click on, and why? The data generated in online news consumption con- stitutes a rich resource ... See full document

... the user could change versions at will using voice ...and user behavior logged automatic- ...and user turns automatically compared to the corres- ponding ASR (one-best) recognized string to pro- duce ... See full document

... The English dataset by Waseem and Hovy (2016) is publicly available on GitHub. 1 The Twitter search API was used to collect the corpus, and in total 16,907 tweets (from 2,399 users) were anno- tated either as ... See full document

... and user-declared metadata using a stacking ...of Twitter data to in- vestigate the impact of temporal factors on model ...that user-declared location metadata is more sensitive to temporal ... See full document

... As Twitter grows larger and larger, finding interesting users to follow becomes an increasingly difficult task, making it a great scenario for the application of recommender ...and user-based ...from ... See full document