35 research outputs found
Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter
This brief communication presents preliminary findings on automated Twitter
accounts distributing links to scientific papers deposited on the preprint
repository arXiv. It discusses the implication of the presence of such bots
from the perspective of social media metrics (altmetrics), where mentions of
scholarly documents on Twitter have been suggested as a means of measuring
impact that is both broader and timelier than citations. We present preliminary
findings that automated Twitter accounts create a considerable amount of tweets
to scientific papers and that they behave differently than common social bots,
which has critical implications for the use of raw tweet counts in research
evaluation and assessment. We discuss some definitions of Twitter cyborgs and
bots in scholarly communication and propose differentiating between different
levels of engagement from tweeting only bibliographic information to discussing
or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl
Bot Spammer Detection in Twitter Using Tweet Similarity and TIME Interval Entropy
The popularity of Twitter has attracted spammers to disseminate large amount of spam messages. Preliminary studies had shown that most spam messages were produced automatically by bot. Therefore bot spammer detection can reduce the number of spam messages in Twitter significantly. However, to the best of our knowledge, few researches have focused in detecting Twitter bot spammer. Thus, this paper proposes a novel approach to differentiate between bot spammer and legitimate user accounts using time interval entropy and tweet similarity. Timestamp collections are utilized to calculate the time interval entropy of each user. Uni-gram matching-based similarity will be used to calculate tweet similarity. Datasets are crawled from Twitter containing both normal and spammer accounts. Experimental results showed that legitimate user may exhibit regular behavior in posting tweet as bot spammer. Several legitimate users are also detected to post similar tweets. Therefore it is less optimal to detect bot spammer using one of those features only. However, combination of both features gives better classification result. Precision, recall, and f-measure of the proposed method reached 85,71%, 94,74% and 90% respectively. It outperforms precision, recall, and f-measure of method which only uses either time interval entropy or tweet similarity
Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph
Twitter is one of the largest social networks using exclusively directed
links among accounts. This makes the Twitter social graph much closer to the
social graph supporting real life communications than, for instance, Facebook.
Therefore, understanding the structure of the Twitter social graph is
interesting not only for computer scientists, but also for researchers in other
fields, such as sociologists. However, little is known about how the
information propagation in Twitter is constrained by its inner structure. In
this paper, we present an in-depth study of the macroscopic structure of the
Twitter social graph unveiling the highways on which tweets propagate, the
specific user activity associated with each component of this macroscopic
structure, and the evolution of this macroscopic structure with time for the
past 6 years. For this study, we crawled Twitter to retrieve all accounts and
all social relationships (follow links) among accounts; the crawl completed in
July 2012 with 505 million accounts interconnected by 23 billion links. Then,
we present a methodology to unveil the macroscopic structure of the Twitter
social graph. This macroscopic structure consists of 8 components defined by
their connectivity characteristics. Each component group users with a specific
usage of Twitter. For instance, we identified components gathering together
spammers, or celebrities. Finally, we present a method to approximate the
macroscopic structure of the Twitter social graph in the past, validate this
method using old datasets, and discuss the evolution of the macroscopic
structure of the Twitter social graph during the past 6 years.Comment: ACM Sigmetrics 2014 (2014
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Data extracted from social networks like Twitter are increasingly being used
to build applications and services that mine and summarize public reactions to
events, such as traffic monitoring platforms, identification of epidemic
outbreaks, and public perception about people and brands. However, such
services are vulnerable to attacks from socialbots automated accounts that
mimic real users seeking to tamper statistics by posting messages generated
automatically and interacting with legitimate users. Potentially, if created in
large scale, socialbots could be used to bias or even invalidate many existing
services, by infiltrating the social networks and acquiring trust of other
users with time. This study aims at understanding infiltration strategies of
socialbots in the Twitter microblogging platform. To this end, we create 120
socialbot accounts with different characteristics and strategies (e.g., gender
specified in the profile, how active they are, the method used to generate
their tweets, and the group of users they interact with), and investigate the
extent to which these bots are able to infiltrate the Twitter social network.
Our results show that even socialbots employing simple automated mechanisms are
able to successfully infiltrate the network. Additionally, using a
factorial design, we quantify infiltration effectiveness of different bot
strategies. Our analysis unveils findings that are key for the design of
detection and counter measurements approaches
Application of the Benford’s law to Social bots and Information Operations activities
Benford\u27s law shows the pattern of behavior in normal systems. It states that in natural systems digits\u27 frequency have a certain pattern such that the occurrence of first digits in numbers are unevenly distributed. In systems with natural behavior, numbers begin with a “1” are more common than numbers beginning with “9”. It implies that if the distribution of first digits deviate from the expected distribution, it is indicative of fraud. It has many applications in forensic accounting, stock markets, finding abnormal data in survey data, and natural science. We investigate whether social media bots and Information Operations activities are conformant to the Benford\u27s law. Our results showed that bots\u27 behavior adhere to Benford\u27s law, suggesting that using this law helps in detecting malicious online automated accounts and their activities on social media. However, activities related to Information Operations did not show consistency in regards to Benford\u27s law. Our findings shedlight on the importance of examining regular and anomalous online behavior to avoid malicious and contaminated content on social media
Incidental effects of automated retweeting: an exploratory network perspective on bot activity during Sri Lanka’s presidential election in 2015
The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. We suggest that the interplay between social media affordances and user practices can result in incidental effects from automated agents. We examined a Twitter network data set with 1,782 nodes and 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction