Twitter has increasingly become a popular platform to share news and user
opinion. A tweet is considered to be important if it receives high number of
affirmative reactions from other Twitter users via Retweets. Retweet count is
thus considered as a surrogate measure for positive crowd-sourced reactions -
high number of retweets of a tweet aid in making its topic trending. This in
turn bolsters the social reputation of the author of the tweet. Since social
reputation/impact of users/tweets influences many decisions (such as promoting
brands, advertisement etc.), several blackmarket syndicates have actively been
engaged in producing fake retweets in a collusive manner. Users who want to
boost the impact of their tweets approach the blackmarket services, and gain
retweets for their own tweets by either paying money (Premium Services) or by
retweeting other customers' tweets. Thus they become customers of blackmarket
syndicates and engage in fake activities. Interestingly, these customers are
neither bots, nor even fake users - they are usually normal human beings; they
express a mix of organic and inorganic retweeting activities, and there is no
synchronicity across their behaviors. In this paper, we make a first attempt to
investigate such blackmarket customers engaged in producing fake retweets. We
collected and annotated a novel dataset comprising of customers of many
blackmarket services and show how their social behavior differs from genuine
users. We then use state-of-the-art supervised models to detect three types of
customers (bots, promotional, normal) and genuine users. We achieve a Macro
F1-score of 0.87 with SVM, outperforming four other baselines significantly. We
further design a browser extension, SCoRe which, given the link of a tweet,
spots its fake retweeters in real-time. We also collected users' feedback on
the performance of SCoRe and obtained 85% accuracy.

本文研究黑市服务中生产虚假转发的 “消费者”，通过机器学习算法对 “消费者” 进行分类，设计了可实时检测虚假转发用户的浏览器插件，并在用户反馈中获得了 85% 的准确率。

转推我们，我们也转推你：发现涉及黑市服务的串通转推者

Retweet Us, We Will Retweet You: Spotting Collusive Retweeters Involved  in Blackmarket Services

We predict the popularity of short messages called tweets created in the
micro-blogging site known as Twitter. We measure the popularity of a tweet by
the time-series path of its retweets, which is when people forward the tweet to
others. We develop a probabilistic model for the evolution of the retweets
using a Bayesian approach, and form predictions using only observations on the
retweet times and the local network or "graph" structure of the retweeters. We
obtain good step ahead forecasts and predictions of the final total number of
retweets even when only a small fraction (i.e., less than one tenth) of the
retweet path is observed. This translates to good predictions within a few
minutes of a tweet being posted, and has potential implications for
understanding the spread of broader ideas, memes, or trends in social networks.

本研究应用贝叶斯方法通过观测 Twitter 上短消息的转发情况和本地网络结构的信息，预测了短消息的传播程度和转发总数，有效提前几分钟预测结果，对于理解社交网络中的广义思想、主题或趋势的传播有潜在的启示作用。

预测推文流行度的贝叶斯方法

A Bayesian approach for predicting the popularity of tweets

We examine the growth, survival, and context of 256 novel hashtags during the
2012 U.S. presidential debates. Our analysis reveals the trajectories of
hashtag use fall into two distinct classes: "winners" that emerge more quickly
and are sustained for longer periods of time than other "also-rans" hashtags.
We propose a "conversational vibrancy" framework to capture dynamics of
hashtags based on their topicality, interactivity, diversity, and prominence.
Statistical analyses of the growth and persistence of hashtags reveal novel
relationships between features of this framework and the relative success of
hashtags. Specifically, retweets always contribute to faster hashtag adoption,
replies extend the life of "winners" while having no effect on "also-rans."
This is the first study on the lifecycle of hashtag adoption and use in
response to purely exogenous shocks. We draw on theories of uses and
gratification, organizational ecology, and language evolution to discuss these
findings and their implications for understanding social influence and
collective action in social media more generally.

研究分析了 2012 年美国总统辩论期间 256 个新兴 hashtags 的生长、生存和背景，发现基于话题性、交互性、多样性和知名度的 “对话活力” 框架可捕捉 hashtags 的动态。统计分析结果显示，转发可以促进 hashtags 更快地被采用，回复对 “优胜者” 的生命周期有延长作用，但对 “落后者” 无影响，这些结果对于更好地理解社交媒体中的社会影响力和集体行动具有重要的启示和影响。