4 research outputs found
Loyalty in Online Communities
Loyalty is an essential component of multi-community engagement. When users
have the choice to engage with a variety of different communities, they often
become loyal to just one, focusing on that community at the expense of others.
However, it is unclear how loyalty is manifested in user behavior, or whether
loyalty is encouraged by certain community characteristics.
In this paper we operationalize loyalty as a user-community relation: users
loyal to a community consistently prefer it over all others; loyal communities
retain their loyal users over time. By exploring this relation using a large
dataset of discussion communities from Reddit, we reveal that loyalty is
manifested in remarkably consistent behaviors across a wide spectrum of
communities. Loyal users employ language that signals collective identity and
engage with more esoteric, less popular content, indicating they may play a
curational role in surfacing new material. Loyal communities have denser
user-user interaction networks and lower rates of triadic closure, suggesting
that community-level loyalty is associated with more cohesive interactions and
less fragmentation into subgroups. We exploit these general patterns to predict
future rates of loyalty. Our results show that a user's propensity to become
loyal is apparent from their first interactions with a community, suggesting
that some users are intrinsically loyal from the very beginning.Comment: Extended version of a paper appearing in the Proceedings of ICWSM
2017 (with the same title); please cite the official ICWSM versio
The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale
In this paper, we interpret the community question answering websites on the
StackExchange platform as knowledge markets, and analyze how and why these
markets can fail at scale. A knowledge market framing allows site operators to
reason about market failures, and to design policies to prevent them. Our goal
is to provide insights on large-scale knowledge market failures through an
interpretable model. We explore a set of interpretable economic production
models on a large empirical dataset to analyze the dynamics of content
generation in knowledge markets. Amongst these, the Cobb-Douglas model best
explains empirical data and provides an intuitive explanation for content
generation through concepts of elasticity and diminishing returns. Content
generation depends on user participation and also on how specific types of
content (e.g. answers) depends on other types (e.g. questions). We show that
these factors of content generation have constant elasticity---a percentage
increase in any of the inputs leads to a constant percentage increase in the
output. Furthermore, markets exhibit diminishing returns---the marginal output
decreases as the input is incrementally increased. Knowledge markets also vary
on their returns to scale---the increase in output resulting from a
proportionate increase in all inputs. Importantly, many knowledge markets
exhibit diseconomies of scale---measures of market health (e.g., the percentage
of questions with an accepted answer) decrease as a function of number of
participants. The implications of our work are two-fold: site operators ought
to design incentives as a function of system size (number of participants); the
market lens should shed insight into complex dependencies amongst different
content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportunities to
study the mechanisms that explain group success. While a growing body of
research investigates community success through a single measure -- typically,
the number of members -- we argue that there are multiple ways of measuring
success. Here, we present a systematic study to understand the relations
between these success definitions and test how well they can be predicted based
on community properties and behaviors from the earliest period of a community's
lifetime. We identify four success measures that are desirable for most
communities: (i) growth in the number of members; (ii) retention of members;
(iii) long term survival of the community; and (iv) volume of activities within
the community. Surprisingly, we find that our measures do not exhibit very high
correlations, suggesting that they capture different types of success.
Additionally, we find that different success measures are predicted by
different attributes of online communities, suggesting that success can be
achieved through different behaviors. Our work sheds light on the basic
understanding of what success represents in online communities and what
predicts it. Our results suggest that success is multi-faceted and cannot be
measured nor predicted by a single measurement. This insight has practical
implications for the creation of new online communities and the design of
platforms that facilitate such communities.Comment: To appear at The Web Conference 201
Discovery, retrieval, and analysis of the 'Star wars' botnet in twitter
It is known that many Twitter users are bots, which are accounts controlled and sometimes created by computers. Twitter bots can send spam tweets, manipulate public opinion and be used for online fraud. Here we report the discovery, retrieval, and analysis of the ‘Star Wars’ botnet in Twitter, which consists of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The botnet contains a single type of bot, showing exactly the same properties throughout the botnet. It is unusually large, many times larger than other available datasets. It provides a valuable source of ground truth for research on Twitter bots. We analysed and revealed rich details on how the botnet was designed and created. As of this writing, the Star Wars bots are still alive in Twitter. They have survived since their creation in 2013, despite the increasing efforts in recent years to detect and remove Twitter bots. We also reflect on the ‘unconventional’ way in which we discovered the Star Wars bots, and discuss the current problems and future challenges of Twitter bot detection