The global dynamics of event cascades are often governed by the local
dynamics of peer influence. However, detecting social influence from
observational data is challenging due to confounds like homophily and practical
issues like missing data. We propose a simple discriminative method to detect
influence from observational data. The core of the approach is to train a
ranking algorithm to predict the source of the next event in a cascade, and
compare its out-of-sample accuracy against a competitive baseline which lacks
access to features corresponding to social influence. We analyze synthetically
generated data to show that this method correctly identifies influence in the
presence of confounds, and is robust to both missing data and misspecification
--- unlike well-known alternatives. We apply the method to two real-world
datasets: (1) the co-sponsorship of legislation in the U.S. House of
Representatives on a social network of shared campaign donors; (2) rumors about
the Higgs boson discovery on a follower network of $10^5$ Twitter accounts. Our
model identifies the role of social influence in these scenarios and uses it to
make more accurate predictions about the future trajectory of cascades.

提出一种基于排名算法的简单判别方法，以检测观测数据中的社交影响，并将其应用于两个真实数据集，包括美国众议院议员在共享竞选捐助者的社交网络上的联合赞助和 $10^5$ 个 Twitter 帐户的追随者网络的关于 Higgs 玻色子发现的谣言。该方法能够准确地识别社交影响的角色并使用其进行更准确的级联未来轨迹预测，且在存在混杂因素、缺失数据和误差模型等情况下具有较强的鲁棒性。

通过比较区分排名器检测事件级联中的社交影响

Detecting Social Influence in Event Cascades by Comparing Discriminative  Rankers

As cryptocurrencies gain popularity and credibility, marketplaces for
cryptocurrencies are growing in importance. Understanding the dynamics of these
markets can help to assess how viable the cryptocurrnency ecosystem is and how
design choices affect market behavior. One existential threat to
cryptocurrencies is dramatic fluctuations in traders' willingness to buy or
sell. Using a novel experimental methodology, we conducted an online experiment
to study how susceptible traders in these markets are to peer influence from
trading behavior. We created bots that executed over one hundred thousand
trades costing less than a penny each in 217 cryptocurrencies over the course
of six months. We find that individual "buy" actions led to short-term
increases in subsequent buy-side activity hundreds of times the size of our
interventions. From a design perspective, we note that the design choices of
the exchange we study may have promoted this and other peer influence effects,
which highlights the potential social and economic impact of HCI in the design
of digital institutions.

通过一项在线实验研究了区块链交易市场中交易者对同侪影响的敏感度以及设计选择对市场行为的影响。结果表明交易者的购买行为会导致购买行为的短期活跃，而交易所的设计会对此产生积极的社会和经济影响。

加密货币市场动态的实验研究

An Experimental Study of Cryptocurrency Market Dynamics

Social influence is the process by which individuals adapt their opinion,
revise their beliefs, or change their behavior as a result of social
interactions with other people. In our strongly interconnected society, social
influence plays a prominent role in many self-organized phenomena such as
herding in cultural markets, the spread of ideas and innovations, and the
amplification of fears during epidemics. Yet, the mechanisms of opinion
formation remain poorly understood, and existing physics-based models lack
systematic empirical validation. Here, we report two controlled experiments
showing how participants answering factual questions revise their initial
judgments after being exposed to the opinion and confidence level of others.
Based on the observation of 59 experimental subjects exposed to peer-opinion
for 15 different items, we draw an influence map that describes the strength of
peer influence during interactions. A simple process model derived from our
observations demonstrates how opinions in a group of interacting people can
converge or split over repeated interactions. In particular, we identify two
major attractors of opinion: (i) the expert effect, induced by the presence of
a highly confident individual in the group, and (ii) the majority effect,
caused by the presence of a critical mass of laypeople sharing similar
opinions. Additional simulations reveal the existence of a tipping point at
which one attractor will dominate over the other, driving collective opinion in
a given direction. These findings have implications for understanding the
mechanisms of public opinion formation and managing conflicting situations in
which self-confident and better informed minorities challenge the views of a
large uninformed majority.

通过两个实验，我们发现被暴露在他人的观点和置信度后，参与者在回答实际问题时会修改其初始判断，建立了一个描述相互作用中同伴影响强度的影响力图，并且识别了两个主要的意见吸引子，即专家效应和多数效应，这些发现对于理解公众舆论形成的机制以及管理存在自信且信息更好的少数派挑战大多数人看法的情况具有重要意义。