21,080 research outputs found
Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns
Twitter has been increasingly used for spreading messages about campaigns.
Such campaigns try to gain followers through their Twitter accounts, influence
the followers and spread messages through them. In this paper, we explore the
relationship between followers sentiment towards the campaign topic and their
rate of retweeting of messages generated by the campaign. Our analysis with
followers of multiple social-media campaigns found statistical significant
correlations between such sentiment and retweeting rate. Based on our analysis,
we have conducted an online intervention study among the followers of different
social-media campaigns. Our study shows that targeting followers based on their
sentiment towards the campaign can give higher retweet rate than a number of
other baseline approaches
Topicality and Social Impact: Diverse Messages but Focused Messengers
Are users who comment on a variety of matters more likely to achieve high
influence than those who delve into one focused field? Do general Twitter
hashtags, such as #lol, tend to be more popular than novel ones, such as
#instantlyinlove? Questions like these demand a way to detect topics hidden
behind messages associated with an individual or a hashtag, and a gauge of
similarity among these topics. Here we develop such an approach to identify
clusters of similar hashtags by detecting communities in the hashtag
co-occurrence network. Then the topical diversity of a user's interests is
quantified by the entropy of her hashtags across different topic clusters. A
similar measure is applied to hashtags, based on co-occurring tags. We find
that high topical diversity of early adopters or co-occurring tags implies high
future popularity of hashtags. In contrast, low diversity helps an individual
accumulate social influence. In short, diverse messages and focused messengers
are more likely to gain impact.Comment: 9 pages, 7 figures, 6 table
Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information
There has been much effort on studying how social media sites, such as
Twitter, help propagate information in different situations, including
spreading alerts and SOS messages in an emergency. However, existing work has
not addressed how to actively identify and engage the right strangers at the
right time on social media to help effectively propagate intended information
within a desired time frame. To address this problem, we have developed two
models: (i) a feature-based model that leverages peoples' exhibited social
behavior, including the content of their tweets and social interactions, to
characterize their willingness and readiness to propagate information on
Twitter via the act of retweeting; and (ii) a wait-time model based on a user's
previous retweeting wait times to predict her next retweeting time when asked.
Based on these two models, we build a recommender system that predicts the
likelihood of a stranger to retweet information when asked, within a specific
time window, and recommends the top-N qualified strangers to engage with. Our
experiments, including live studies in the real world, demonstrate the
effectiveness of our work
Traffic event detection framework using social media
This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595
The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio
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