7,053 research outputs found
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network
platforms nowadays, aiming to maximize user satisfaction along with other key
business objectives. Twitter is no exception. Despite the fact that Twitter
data has been extensively used to understand socioeconomic and political
phenomena and user behaviour, the implicit feedback provided by users on Tweets
through their engagements on the Home Timeline has only been explored to a
limited extent. At the same time, there is a lack of large-scale public social
network datasets that would enable the scientific community to both benchmark
and build more powerful and comprehensive models that tailor content to user
interests. By releasing an original dataset of 160 million Tweets along with
engagement information, Twitter aims to address exactly that. During this
release, special attention is drawn on maintaining compliance with existing
privacy laws. Apart from user privacy, this paper touches on the key challenges
faced by researchers and professionals striving to predict user engagements. It
further describes the key aspects of the RecSys 2020 Challenge that was
organized by ACM RecSys in partnership with Twitter using this dataset.Comment: 16 pages, 2 table
Regression and Learning to Rank Aggregation for User Engagement Evaluation
User engagement refers to the amount of interaction an instance (e.g., tweet,
news, and forum post) achieves. Ranking the items in social media websites
based on the amount of user participation in them, can be used in different
applications, such as recommender systems. In this paper, we consider a tweet
containing a rating for a movie as an instance and focus on ranking the
instances of each user based on their engagement, i.e., the total number of
retweets and favorites it will gain.
For this task, we define several features which can be extracted from the
meta-data of each tweet. The features are partitioned into three categories:
user-based, movie-based, and tweet-based. We show that in order to obtain good
results, features from all categories should be considered. We exploit
regression and learning to rank methods to rank the tweets and propose to
aggregate the results of regression and learning to rank methods to achieve
better performance. We have run our experiments on an extended version of
MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show
that learning to rank approach outperforms most of the regression models and
the combination can improve the performance significantly.Comment: In Proceedings of the 2014 ACM Recommender Systems Challenge,
RecSysChallenge '1
Cascades: A view from Audience
Cascades on online networks have been a popular subject of study in the past
decade, and there is a considerable literature on phenomena such as diffusion
mechanisms, virality, cascade prediction, and peer network effects. However, a
basic question has received comparatively little attention: how desirable are
cascades on a social media platform from the point of view of users? While
versions of this question have been considered from the perspective of the
producers of cascades, any answer to this question must also take into account
the effect of cascades on their audience. In this work, we seek to fill this
gap by providing a consumer perspective of cascade.
Users on online networks play the dual role of producers and consumers.
First, we perform an empirical study of the interaction of Twitter users with
retweet cascades. We measure how often users observe retweets in their home
timeline, and observe a phenomenon that we term the "Impressions Paradox": the
share of impressions for cascades of size k decays much slower than frequency
of cascades of size k. Thus, the audience for cascades can be quite large even
for rare large cascades. We also measure audience engagement with retweet
cascades in comparison to non-retweeted content. Our results show that cascades
often rival or exceed organic content in engagement received per impression.
This result is perhaps surprising in that consumers didn't opt in to see tweets
from these authors. Furthermore, although cascading content is widely popular,
one would expect it to eventually reach parts of the audience that may not be
interested in the content. Motivated by our findings, we posit a theoretical
model that focuses on the effect of cascades on the audience. Our results on
this model highlight the balance between retweeting as a high-quality content
selection mechanism and the role of network users in filtering irrelevant
content
An analysis of UK Policing Engagement via Social Media
Police forces in the UK make use of social media to communicate and engage with the public. However, while guidance reports claim that social media can enhance the accessibility of policing organisations, research studies have shown that exchanges between the citizens and the police tend to be infrequent. Social media usually act as an extra channel for delivering messages, but not as a mean for enabling a deeper engagement with the public. This has led to a phenomena where police officers and staff started to use social media in a personal capacity in the aim of getting closer to the public. In this paper, we aim to understand what attracts citizens to engage with social media policing content, from corporate as well as from non-corporate accounts. Our approach combines learnings from existing theories and studies on user engagement as well as from the analysis of 1.5 Million posts from 48 corporate and 2,450 non-corporate Twitter police accounts. Our results provide police-specific guidelines on how to improve communication to increase public engagement and participation
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