scholarly journals Extracting Interest Tags from Twitter User Biographies

2014 ◽  
pp. 268-279 ◽  
Author(s):  
Ying Ding ◽  
Jing Jiang
Keyword(s):  
Twitter User

scholarly journals A Practical Guide for the Effective Evaluation of Twitter User Geolocation

2019 ◽  
Vol 2 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Ahmed Mourad ◽  
Falk Scholer ◽  
Walid Magdy ◽  
Mark Sanderson
Keyword(s):  
Effective Evaluation ◽  
Practical Guide ◽  
Twitter User

Twitter User Recommendation for Gaining Followers

2018 ◽  
pp. 539-552
Author(s):  
Francesco Corcoglioniti ◽  
Yaroslav Nechaev ◽  
Claudio Giuliano ◽  
Roberto Zanoli
Keyword(s):  
Twitter User ◽  
User Recommendation

scholarly journals Interpreting Twitter User Geolocation

2020 ◽  
Author(s):  
Ting Zhong ◽  
Tianliang Wang ◽  
Fan Zhou ◽  
Goce Trajcevski ◽  
Kunpeng Zhang ◽  
...  
Keyword(s):  
Twitter User

scholarly journals Locked down, but not isolated: Twitter collaboration among teachers in response to COVID-19

2021 ◽  
pp. 71-77
Author(s):  
Fernando Rosell-Aguilar
Keyword(s):  
Online Learning ◽  
Good Practice ◽  
Online Pedagogy ◽  
Education Professionals ◽  
General Overview ◽  
Twitter User

This piece looks at the use of Twitter to share good practice among education professionals responding to the so-called ‘pivot online’: the sudden shift to online learning necessitated by the spread of the Coronavirus pandemic. It presents a general overview on how Twitter provided a source of advice, ideas, and resources and how teachers shared their expertise at this time of need, focusing on my own experience as a Twitter user and online pedagogy expert.

scholarly journals Visualization of the Artist Relations Using Twitter User Profiles

2020 ◽  
Author(s):  
Minoru Yoshida ◽  
Shogo Kohno ◽  
Kazuyuki Matsumoto ◽  
Kenji Kita
Keyword(s):  
User Profile ◽  
Word Embedding ◽  
New Music ◽  
User Profiles ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Twitter User ◽  
Streaming Services ◽  
Music Streaming

We propose a new music artist recommendation algorithm using Twitter profile texts. Today, music recommendation is provided in many music streaming services. In this paper, we propose a new recommendation algorithm for this music recommendation task. Our idea is to use Twitter profile texts to find appropriate artist names to recommend. We obtained word embedding vectors for each artist name by applying word2vec algorithm to the corpus obtained by collecting such user profile texts, resulting in vectors that reflect artist co-occurrence in the profile texts.

Analyzing Twitter User Behaviors and Topic Trends by Exploiting Dynamic Rules

2012 ◽  
pp. 267-287 ◽  
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
User Behaviors ◽  
Twitter User
Sign in / Sign up

Export Citation Format

Share Document