697 research outputs found
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science
Track, Perth, Australia, 3-7 April, 2017
๊ฐ์ธ ์ฌํ๋ง ๋คํธ์ํฌ ๋ถ์ ๊ธฐ๋ฐ ์จ๋ผ์ธ ์ฌํ ๊ณต๊ฒฉ์ ํ์ง
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ๊น์ข
๊ถ.In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, Weibo and LinkedIn. While SNSs provide diverse benefits โ for example, fostering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with spamming in Twitter and Weibo. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) normal users, and followed a normal user. Sometimes a spammer makes link farm to increase target accounts explicit influence. Based on the assumption that the online relationships of spammers are different from those of normal users, I proposed classification schemes that detect online social attackers including spammers. I firstly focused on ego-network social relations and devised two features, structural features based on Triad Significance Profile (TSP) and relational semantic features based on hierarchical homophily in an ego-network. Experiments on real Twitter and Weibo datasets demonstrated that the proposed approach is very practical. The proposed features are scalable because instead of analyzing the whole network, they inspect user-centered ego-networks. My performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.์ต๊ทผ ์ฐ๋ฆฌ๋ Facebook, Twitter, Weibo, LinkedIn ๋ฑ์ ๋ค์ํ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๊ฐ ํญ๋ฐ์ ์ผ๋ก ์ฑ์ฅํ๋ ํ์์ ๋ชฉ๊ฒฉํ์๋ค. ํ์ง๋ง ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๊ฐ ๊ฐ์ธ๊ณผ ๊ฐ์ธ๊ฐ์ ๊ด๊ณ ๋ฐ ์ปค๋ฎค๋ํฐ ํ์ฑ๊ณผ ๋ด์ค ์ ํ ๋ฑ์ ์ฌ๋ฌ ์ด์ ์ ์ ๊ณตํด ์ฃผ๊ณ ์๋๋ฐ ๋ฐํด ๋ฐ๊ฐ์ง ์์ ํ์ ์ญ์ ๋ฐ์ํ๊ณ ์๋ค. ์คํจ๋จธ๋ค์ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๋ฅผ ๋๋ ฅ ์ผ์ ์คํธ์ ๋งค์ฐ ๋น ๋ฅด๊ณ ๋๊ฒ ์ ํํ๋ ์์ผ๋ก ์
์ฉํ๊ณ ์๋ค. ์คํธ์ ์์ ์๊ฐ ์์น ์๋ ๋ฉ์์ง๋ค์ ์ผ์ปฝ๋๋ฐ ์ด๋ ์๋น์ค์ ์ ๋ขฐ๋์ ์์ ์ฑ์ ํฌ๊ฒ ์์์ํจ๋ค. ๋ฐ๋ผ์, ์คํจ๋จธ๋ฅผ ํ์งํ๋ ๊ฒ์ด ํ์ฌ ์์
๋ฏธ๋์ด์์ ๋งค์ฐ ๊ธด๊ธํ๊ณ ์ค์ํ ๋ฌธ์ ๊ฐ ๋์๋ค. ์ด ๋
ผ๋ฌธ์ ๋ํ์ ์ธ ์ฌํ ๊ด๊ณ๋ง ์๋น์ค๋ค ์ค Twitter์ Weibo์์ ๋ฐ์ํ๋ ์คํจ๋ฐ์ ๋ค๋ฃจ๊ณ ์๋ค. ์ด๋ฌํ ์ ํ์ ์คํจ๋ฐ๋ค์ ๋ถํน์ ๋ค์์๊ฒ ๋ฉ์์ง๋ฅผ ์ ํํ๋ ๋์ ์, ๋ง์ ์ผ๋ฐ ์ฌ์ฉ์๋ค์ 'ํ๋ก์ฐ(๊ตฌ๋
)'ํ๊ณ ์ด๋ค๋ก๋ถํฐ '๋ง ํ๋ก์(๋ง ๊ตฌ๋
)'์ ์ด๋์ด ๋ด๋ ๊ฒ์ ๋ชฉ์ ์ผ๋ก ํ๊ธฐ๋ ํ๋ค. ๋๋ก๋ link farm์ ์ด์ฉํด ํน์ ๊ณ์ ์ ํ๋ก์ ์๋ฅผ ๋์ด๊ณ ๋ช
์์ ์ํฅ๋ ฅ์ ์ฆ๊ฐ์ํค๊ธฐ๋ ํ๋ค. ์คํจ๋จธ์ ์จ๋ผ์ธ ๊ด๊ณ๋ง์ด ์ผ๋ฐ ์ฌ์ฉ์์ ์จ๋ผ์ธ ์ฌํ๋ง๊ณผ ๋ค๋ฅผ ๊ฒ์ด๋ผ๋ ๊ฐ์ ํ์, ๋๋ ์คํจ๋จธ๋ค์ ํฌํจํ ์ผ๋ฐ์ ์ธ ์จ๋ผ์ธ ์ฌํ๋ง ๊ณต๊ฒฉ์๋ค์ ํ์งํ๋ ๋ถ๋ฅ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ๋๋ ๋จผ์ ๊ฐ์ธ ์ฌํ๋ง ๋ด ์ฌํ ๊ด๊ณ์ ์ฃผ๋ชฉํ๊ณ ๋ ๊ฐ์ง ์ข
๋ฅ์ ๋ถ๋ฅ ํน์ฑ์ ์ ์ํ์๋ค. ์ด๋ค์ ๊ฐ์ธ ์ฌํ๋ง์ Triad Significance Profile (TSP)์ ๊ธฐ๋ฐํ ๊ตฌ์กฐ์ ํน์ฑ๊ณผ Hierarchical homophily์ ๊ธฐ๋ฐํ ๊ด๊ณ ์๋ฏธ์ ํน์ฑ์ด๋ค. ์ค์ Twitter์ Weibo ๋ฐ์ดํฐ์
์ ๋ํ ์คํ ๊ฒฐ๊ณผ๋ ์ ์ํ ๋ฐฉ๋ฒ์ด ๋งค์ฐ ์ค์ฉ์ ์ด๋ผ๋ ๊ฒ์ ๋ณด์ฌ์ค๋ค. ์ ์ํ ํน์ฑ๋ค์ ์ ์ฒด ๋คํธ์ํฌ๋ฅผ ๋ถ์ํ์ง ์์๋ ๊ฐ์ธ ์ฌํ๋ง๋ง ๋ถ์ํ๋ฉด ๋๊ธฐ ๋๋ฌธ์ scalableํ๊ฒ ์ธก์ ๋ ์ ์๋ค. ๋์ ์ฑ๋ฅ ๋ถ์ ๊ฒฐ๊ณผ๋ ์ ์ํ ๊ธฐ๋ฒ์ด ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๋นํด true positive์ false positive ์ธก๋ฉด์์ ์ฐ์ํ๋ค๋ ๊ฒ์ ๋ณด์ฌ์ค๋ค.1 Introduction 1
2 Related Work 6
2.1 OSN Spammer Detection Approaches 6
2.1.1 Contents-based Approach 6
2.1.2 Social Network-based Approach 7
2.1.3 Subnetwork-based Approach 8
2.1.4 Behavior-based Approach 9
2.2 Link Spam Detection 10
2.3 Data mining schemes for Spammer Detection 10
2.4 Sybil Detection 12
3 Triad Significance Profile Analysis 14
3.1 Motivation 14
3.2 Twitter Dataset 18
3.3 Indegree and Outdegree of Dataset 20
3.4 Twitter spammer Detection with TSP 22
3.5 TSP-Filtering 27
3.6 Performance Evaluation of TSP-Filtering 29
4 Hierarchical Homophily Analysis 33
4.1 Motivation 33
4.2 Hierarchical Homophily in OSN 37
4.2.1 Basic Analysis of Datasets 39
4.2.2 Status gap distribution and Assortativity 44
4.2.3 Hierarchical gap distribution 49
4.3 Performance Evaluation of HH-Filtering 53
5 Overall Performance Evaluation 58
6 Conclusion 63
Bibliography 65Docto
Probabilistic Matching: Causal Inference under Measurement Errors
The abundance of data produced daily from large variety of sources has
boosted the need of novel approaches on causal inference analysis from
observational data. Observational data often contain noisy or missing entries.
Moreover, causal inference studies may require unobserved high-level
information which needs to be inferred from other observed attributes. In such
cases, inaccuracies of the applied inference methods will result in noisy
outputs. In this study, we propose a novel approach for causal inference when
one or more key variables are noisy. Our method utilizes the knowledge about
the uncertainty of the real values of key variables in order to reduce the bias
induced by noisy measurements. We evaluate our approach in comparison with
existing methods both on simulated and real scenarios and we demonstrate that
our method reduces the bias and avoids false causal inference conclusions in
most cases.Comment: In Proceedings of International Joint Conference Of Neural Networks
(IJCNN) 201
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