26 research outputs found
The Dynamics of Health Behavior Sentiments on a Large Online Social Network
Modifiable health behaviors, a leading cause of illness and death in many
countries, are often driven by individual beliefs and sentiments about health
and disease. Individual behaviors affecting health outcomes are increasingly
modulated by social networks, for example through the associations of
like-minded individuals - homophily - or through peer influence effects. Using
a statistical approach to measure the individual temporal effects of a large
number of variables pertaining to social network statistics, we investigate the
spread of a health sentiment towards a new vaccine on Twitter, a large online
social network. We find that the effects of neighborhood size and exposure
intensity are qualitatively very different depending on the type of sentiment.
Generally, we find that larger numbers of opinionated neighbors inhibit the
expression of sentiments. We also find that exposure to negative sentiment is
contagious - by which we merely mean predictive of future negative sentiment
expression - while exposure to positive sentiments is generally not. In fact,
exposure to positive sentiments can even predict increased negative sentiment
expression. Our results suggest that the effects of peer influence and social
contagion on the dynamics of behavioral spread on social networks are strongly
content-dependent
The dynamics of health behavior sentiments on a large online social network
Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependen
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
The arm race between spambots and spambot-detectors is made of several cycles
(or generations): a new wave of spambots is created (and new spam is spread),
new spambot filters are derived and old spambots mutate (or evolve) to new
species. Recently, with the diffusion of the adversarial learning approach, a
new practice is emerging: to manipulate on purpose target samples in order to
make stronger detection models. Here, we manipulate generations of Twitter
social bots, to obtain - and study - their possible future evolutions, with the
aim of eventually deriving more effective detection techniques. In detail, we
propose and experiment with a novel genetic algorithm for the synthesis of
online accounts. The algorithm allows to create synthetic evolved versions of
current state-of-the-art social bots. Results demonstrate that synthetic bots
really escape current detection techniques. However, they give all the needed
elements to improve such techniques, making possible a proactive approach for
the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM
Conference on Web Science, June 30-July 3, 2019, Boston, U
Mass Media and the Contagion of Fear: The Case of Ebola in America
abstract: Background
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.The article is published at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.012917
The amplification of risk in experimental diffusion chains
Understanding how people form and revise their perception of risk is central
to designing efficient risk communication methods, eliciting risk awareness,
and avoiding unnecessary anxiety among the public. However, public responses to
hazardous events such as climate change, contagious outbreaks, and terrorist
threats are complex and difficult-to-anticipate phenomena. Although many
psychological factors influencing risk perception have been identified in the
past, it remains unclear how perceptions of risk change when propagated from
one person to another and what impact the repeated social transmission of
perceived risk has at the population scale. Here, we study the social dynamics
of risk perception by analyzing how messages detailing the benefits and harms
of a controversial antibacterial agent undergo change when passed from one
person to the next in 10-subject experimental diffusion chains. Our analyses
show that when messages are propagated through the diffusion chains, they tend
to become shorter, gradually inaccurate, and increasingly dissimilar between
chains. In contrast, the perception of risk is propagated with higher fidelity
due to participants manipulating messages to fit their preconceptions, thereby
influencing the judgments of subsequent participants. Computer simulations
implementing this simple influence mechanism show that small judgment biases
tend to become more extreme, even when the injected message contradicts
preconceived risk judgments. Our results provide quantitative insights into the
social amplification of risk perception, and can help policy makers better
anticipate and manage the public response to emerging threats.Comment: Published online in PNAS Early Edition (open-access):
http://www.pnas.org/content/early/2015/04/14/142188311
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls
Large-scale databases of human activity in social media have captured
scientific and policy attention, producing a flood of research and discussion.
This paper considers methodological and conceptual challenges for this emergent
field, with special attention to the validity and representativeness of social
media big data analyses. Persistent issues include the over-emphasis of a
single platform, Twitter, sampling biases arising from selection by hashtags,
and vague and unrepresentative sampling frames. The socio-cultural complexity
of user behavior aimed at algorithmic invisibility (such as subtweeting,
mock-retweeting, use of "screen captures" for text, etc.) further complicate
interpretation of big data social media. Other challenges include accounting
for field effects, i.e. broadly consequential events that do not diffuse only
through the network under study but affect the whole society. The application
of network methods from other fields to the study of human social activity may
not always be appropriate. The paper concludes with a call to action on
practical steps to improve our analytic capacity in this promising,
rapidly-growing field.Comment: Tufekci, Zeynep. (2014). Big Questions for Social Media Big Data:
Representativeness, Validity and Other Methodological Pitfalls. In ICWSM '14:
Proceedings of the 8th International AAAI Conference on Weblogs and Social
Media, 2014. [forthcoming