2,206 research outputs found
Where are my followers? Understanding the Locality Effect in Twitter
Twitter is one of the most used applications in the current Internet with
more than 200M accounts created so far. As other large-scale systems Twitter
can obtain enefit by exploiting the Locality effect existing among its users.
In this paper we perform the first comprehensive study of the Locality effect
of Twitter. For this purpose we have collected the geographical location of
around 1M Twitter users and 16M of their followers. Our results demonstrate
that language and cultural characteristics determine the level of Locality
expected for different countries. Those countries with a different language
than English such as Brazil typically show a high intra-country Locality
whereas those others where English is official or co-official language suffer
from an external Locality effect. This is, their users have a larger number of
followers in US than within their same country. This is produced by two
reasons: first, US is the dominant country in Twitter counting with around half
of the users, and second, these countries share a common language and cultural
characteristics with US
Predicting Rising Follower Counts on Twitter Using Profile Information
When evaluating the cause of one's popularity on Twitter, one thing is
considered to be the main driver: Many tweets. There is debate about the kind
of tweet one should publish, but little beyond tweets. Of particular interest
is the information provided by each Twitter user's profile page. One of the
features are the given names on those profiles. Studies on psychology and
economics identified correlations of the first name to, e.g., one's school
marks or chances of getting a job interview in the US. Therefore, we are
interested in the influence of those profile information on the follower count.
We addressed this question by analyzing the profiles of about 6 Million Twitter
users. All profiles are separated into three groups: Users that have a first
name, English words, or neither of both in their name field. The assumption is
that names and words influence the discoverability of a user and subsequently
his/her follower count. We propose a classifier that labels users who will
increase their follower count within a month by applying different models based
on the user's group. The classifiers are evaluated with the area under the
receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy,
NY, US
A customisable pipeline for continuously harvesting socially-minded Twitter users
On social media platforms and Twitter in particular, specific classes of
users such as influencers have been given satisfactory operational definitions
in terms of network and content metrics.
Others, for instance online activists, are not less important but their
characterisation still requires experimenting.
We make the hypothesis that such interesting users can be found within
temporally and spatially localised contexts, i.e., small but topical fragments
of the network containing interactions about social events or campaigns with a
significant footprint on Twitter.
To explore this hypothesis, we have designed a continuous user profile
discovery pipeline that produces an ever-growing dataset of user profiles by
harvesting and analysing contexts from the Twitter stream.
The profiles dataset includes key network and content-based users metrics,
enabling experimentation with user-defined score functions that characterise
specific classes of online users.
The paper describes the design and implementation of the pipeline and its
empirical evaluation on a case study consisting of healthcare-related campaigns
in the UK, showing how it supports the operational definitions of online
activism, by comparing three experimental ranking functions. The code is
publicly available.Comment: Procs. ICWE 2019, June 2019, Kore
Fame for sale: efficient detection of fake Twitter followers
are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and society. In this paper, we contribute along different
dimensions. First, we review some of the most relevant existing features and
rules (proposed by Academia and Media) for anomalous Twitter accounts
detection. Second, we create a baseline dataset of verified human and fake
follower accounts. Such baseline dataset is publicly available to the
scientific community. Then, we exploit the baseline dataset to train a set of
machine-learning classifiers built over the reviewed rules and features. Our
results show that most of the rules proposed by Media provide unsatisfactory
performance in revealing fake followers, while features proposed in the past by
Academia for spam detection provide good results. Building on the most
promising features, we revise the classifiers both in terms of reduction of
overfitting and cost for gathering the data needed to compute the features. The
final result is a novel classifier, general enough to thwart
overfitting, lightweight thanks to the usage of the less costly features, and
still able to correctly classify more than 95% of the accounts of the original
training set. We ultimately perform an information fusion-based sensitivity
analysis, to assess the global sensitivity of each of the features employed by
the classifier. The findings reported in this paper, other than being supported
by a thorough experimental methodology and interesting on their own, also pave
the way for further investigation on the novel issue of fake Twitter followers
A Data-driven Study of Influences in Twitter Communities
This paper presents a quantitative study of Twitter, one of the most popular
micro-blogging services, from the perspective of user influence. We crawl
several datasets from the most active communities on Twitter and obtain 20.5
million user profiles, along with 420.2 million directed relations and 105
million tweets among the users. User influence scores are obtained from
influence measurement services, Klout and PeerIndex. Our analysis reveals
interesting findings, including non-power-law influence distribution, strong
reciprocity among users in a community, the existence of homophily and
hierarchical relationships in social influences. Most importantly, we observe
that whether a user retweets a message is strongly influenced by the first of
his followees who posted that message. To capture such an effect, we propose
the first influencer (FI) information diffusion model and show through
extensive evaluation that compared to the widely adopted independent cascade
model, the FI model is more stable and more accurate in predicting influence
spreads in Twitter communities.Comment: 11 page
Bridging the Gap Between the Least and the Most Influential Twitter Users
Social networks play an increasingly important role in shaping the behaviour of users of the Web. Conceivably Twitter stands out from the others, not only for the platform's simplicity but also for the great influence that the messages sent over the network can have. The impact of such messages determines the influence of a Twitter user and is what tools such as Klout, PeerIndex or TwitterGrader aim to calculate. Reducing all the factors that make a person influential into a single number is not an easy task, and the effort involved could become useless if the Twitter users do not know how to improve it. In this paper we identify what specific actions should be carried out for a Twitterer to increase their influence in each of above-mentioned tools applying, for this purpose, data mining techniques based on classification and regression algorithms to the information collected from a set of Twitter users.This work has been partially founded by the European Commission Project âSiSOB: An Observatorium for Science
in Society based in Social Modelsâ (http://sisob.lcc.uma.es) (Contract no.: FP7 266588), âSistemas InalĂĄmbricos
de GestiĂłn de InformaciĂłn CrĂticaâ (with code number TIN2011-23795 and granted by the MEC, Spain) and â3DTUTOR:
Sistema Interoperable de Asistencia y TutorĂa Virtual e Inteligente 3Dâ (with code number IPT-2011-0889-
900000 and granted by the MINECO, Spain
The ISIS Twitter census: defining and describing the population of ISIS supporters on Twitter
Presents a demographic snapshot of ISIS supporters on Twitter by analysing a sample of 20,000 ISIS-supporting Twitter accounts, mapping the locations, preferred languages, and the number and type of followers of these accounts.
Overview
Although much ink has been spilled on ISISâs activity on Twitter, very basic questions about the groupâs social media strategy remain unanswered. In a new analysis paper, J.M. Berger and Jonathon Morgan answer fundamental questions about how many Twitter users support ISIS, who and where they are, and how they participate in its highly organized online activities.
Previous analyses of ISISâs Twitter reach have relied on limited segments of the overall ISIS social network. The small, cellular nature of that networkâand the focus on particular subsets within the network such as foreign fightersâmay create misleading conclusions. This information vacuum extends to discussions of how the West should respond to the groupâs online campaigns.
Berger and Morgan present a demographic snapshot of ISIS supporters on Twitter by analyzing a sample of 20,000 ISIS-supporting Twitter accounts. Using a sophisticated and innovative methodology, the authors map the locations, preferred languages, and the number and type of followers of these accounts.
Among the key findings:
From September through December 2014, the authors estimate that at least 46,000 Twitter accounts were used by ISIS supporters, although not all of them were active at the same time.
Typical ISIS supporters were located within the organizationâs territories in Syria and Iraq, as well as in regions contested by ISIS. Hundreds of ISIS-supporting accounts sent tweets with location metadata embedded.
Almost one in five ISIS supporters selected English as their primary language when using Twitter. Three quarters selected Arabic.
ISIS-supporting accounts had an average of about 1,000 followers each, considerably higher than an ordinary Twitter user. ISIS-supporting accounts were also considerably more active than non-supporting users.
A minimum of 1,000 ISIS-supporting accounts were suspended by Twitter between September and December 2014. Accounts that tweeted most often and had the most followers were most likely to be suspended.
Much of ISISâs social media success can be attributed to a relatively small group of hyperactive users, numbering between 500 and 2,000 accounts, which tweet in concentrated bursts of high volume.
Based on their key findings, the authors recommend social media companies and the U.S government work together to devise appropriate responses to extremism on social media. Approaches to the problem of extremist use of social media, Berger and Morgan contend, are most likely to succeed when they are mainstreamed into wider dialogues among the broad range of community, private, and public stakeholders
Hot Streaks on Social Media
Measuring the impact and success of human performance is common in various
disciplines, including art, science, and sports. Quantifying impact also plays
a key role on social media, where impact is usually defined as the reach of a
user's content as captured by metrics such as the number of views, likes,
retweets, or shares. In this paper, we study entire careers of Twitter users to
understand properties of impact. We show that user impact tends to have certain
characteristics: First, impact is clustered in time, such that the most
impactful tweets of a user appear close to each other. Second, users commonly
have 'hot streaks' of impact, i.e., extended periods of high-impact tweets.
Third, impact tends to gradually build up before, and fall off after, a user's
most impactful tweet. We attempt to explain these characteristics using various
properties measured on social media, including the user's network, content,
activity, and experience, and find that changes in impact are associated with
significant changes in these properties. Our findings open interesting avenues
for future research on virality and influence on social media.Comment: Accepted as a full paper at ICWSM 2019. Please cite the ICWSM versio
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