Bin GUO1(), Yasan DING1, Yueheng SUN2, Shuai MA3, Ke LI1, Zhiwen YU1
1. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China 2. School of Cyber Security, Tianjin University, Tianjin 300350, China 3. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
The widespread fake news in social networks is posing threats to social stability, economic development, and political democracy, etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the intrinsic propagation and cognition mechanisms of fake news. Since the development of cognitive science paves a promising way for the prevention of fake news, we present a new research area called Cognition Security (CogSec), which studies the potential impacts of fake news on human cognition, ranging from misperception, untrusted knowledge acquisition, targeted opinion/attitude formation, to biased decision making, and investigates the effective ways for fake news debunking. CogSec is a multidisciplinary research field that leverages the knowledge from social science, psychology, cognition science, neuroscience, AI and computer science. We first propose related definitions to characterize CogSec and review the literature history. We further investigate the key research challenges and techniques of CogSec, including humancontent cognition mechanism, social influence and opinion diffusion, fake news detection, and malicious bot detection. Finally, we summarize the open issues and future research directions, such as the cognition mechanism of fake news, influence maximization of fact-checking information, early detection of fake news, fast refutation of fake news, and so on.
S Iyengar, D S Massey. Scientific communication in a post-truth society. Proceedings of the National Academy of Sciences, 2019, 116(16): 7656–7661 https://doi.org/10.1073/pnas.1805868115
2
M Fernandez, H Alani. Online misinformation: challenges and future directions. In: Proceedings of the Web Conference. 2018, 595–602 https://doi.org/10.1145/3184558.3188730
3
A Guess, B Nyhan, J Reifler. Selective exposure to misinformation: evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council, 2018, 9(3): 4–52
4
X Zhou, R Zafarani, K Shu, H Liu. Fake news: fundamental theories, detection strategies and challenges. In: Proceedings of the ACM International Conference on Web Search and Data Mining. 2019, 836–837 https://doi.org/10.1145/3289600.3291382
D M J Lazer, M A Baum, Y Benkler, A J Berinsky, K M Greenhill, F Menczer, M J Metzger, B Nyhan, G Pennycook, D Rothschild. The science of fake news. Science, 2018, 359(6380): 1094–1096 https://doi.org/10.1126/science.aao2998
X Qiu, D F M Oliveira, A S Shirazi, A Flammini, F Menczer. Limited individual attention and online virality of low-quality information. Nature Human Behaviour, 2017, 1(7): 0132 https://doi.org/10.1038/s41562-017-0132
9
J Bakdash, C Sample, M Rankin, M Kantarcioglu, J Holmes, S Kase, E Zaroukian, B Szymanski. The future of deception: machine-generated and manipulated images, video, and audio?. In: Proceedings of IEEE International Workshop on Social Sensing. 2018 https://doi.org/10.1109/SocialSens.2018.00009
X Yang, Y Li, S Lyu. Exposing deep fakes using inconsistent head poses. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2019, 8261–8265 https://doi.org/10.1109/ICASSP.2019.8683164
12
S Agarwal, H Farid, Y Gu, M He, K Nagano, H Li. Protecting world leaders against deep fakes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019, 38–45
A Zubiaga, A Aker, K Bontcheva, M Liakata, R Procter. Detection and resolution of rumours in social media: a survey. ACM Computing Surveys (CSUR), 2018, 51(2): 32–67 https://doi.org/10.1145/3161603
15
S Kumar, R West, J Leskovec. Disinformation on the web: impact, characteristics, and detection of wikipedia hoaxes. In: Proceedings of International Conference on World Wide Web. 2016, 591–602 https://doi.org/10.1145/2872427.2883085
16
S Volkova, K Shaffer, J Y Jang, N Hodas. Separating facts from fiction: linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017, 647–653 https://doi.org/10.18653/v1/P17-2102
17
L Wu, F Morstatter, X Hu, H Liu. Big Data in Complex and Social Networks. 1st ed. London: Chapman and Hall/CRC, 2016
18
K Shu, A Sliva, S Wang, J Tang, H Liu. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsletter, 2017, 19(1): 22–36 https://doi.org/10.1145/3137597.3137600
19
X Zhou, R Zafarani. Fake news: a survey of research, detection methods, and opportunities. 2018, arXiv preprint arXiv:1812.00315
20
S B Jr, G F Campos, G M Tavares, R A Igawa, M L P Jr, R C Guido. Detection of human, legitimate bot, and malicious bot in online social networks based on wavelets. ACM Transactions on Multimedia Computing, Communications, and Applications, 2018, 14(1s): 26–42 https://doi.org/10.1145/3183506
21
S A Macskassy. On the study of social interactions in twitter. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. 2012, 226–233
22
B A Forouzan. Cryptography & Network Security. New York: McGraw-Hill, 2007
23
R Greenstadt, J Beal. Cognitive security for personal devices. In: Proceedings of ACM Workshop on AISec. 2008, 27–30 https://doi.org/10.1145/1456377.1456383
24
W Kinsner. Towards cognitive security systems. In: Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing. 2012, 539
C Vaccari. From echo chamber to persuasive device? rethinking the role of the Internet in campaigns. New Media & Society, 2013, 15(1): 109–127 https://doi.org/10.1177/1461444812457336
27
S Flaxman, S Goel, J M Rao. Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 2016, 80(S1): 298–320 https://doi.org/10.1093/poq/nfw006
28
M Flintham, C Karner, K Bachour, H Creswick, N Gupta, S Moran. Falling for fake news: investigating the consumption of news via social media. In: Proceedings of CHI Conference on Human Factors in Computing Systems. 2018, 376–385 https://doi.org/10.1145/3173574.3173950
29
P Barberá, J T Jost, J Nagler, J A Tucker, R Bonneau. Tweeting from left to right: is online political communication more than an echo chamber?. Psychological Science, 2015, 26(10): 1531–1542 https://doi.org/10.1177/0956797615594620
R B Zajonc. Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 1968, 9(2p2): 1–27 https://doi.org/10.1037/h0025848
32
M Del Vicario, A Bessi, F Zollo, F Petroni, A Scala, G Caldarelli, H E Stanley, W Quattrociocchi. The spreading of misinformation online. Proceedings of the National Academy of Sciences, 2016, 113(3): 554–559 https://doi.org/10.1073/pnas.1517441113
R K Nielsen. News Media, Search Engines and Social Networking Sites as Varieties of Online Gatekeepers. Rethinking Journalism Again. London: Routledge, 2016
35
C L Bui. How online gatekeepers guard our view-news portals’ inclusion and ranking of media and events. Global Media Journal, 2010, 9(16): N_A
36
W Xu, M Feng. Talking to the broadcasters on twitter: networked gatekeeping in twitter conversations with journalists. Journal of Broadcasting & Electronic Media, 2014, 58(3): 420–437 https://doi.org/10.1080/08838151.2014.935853
37
K Garimella, G De Francisci Morales, A Gionis, M Mathioudakis. Political discourse on social media: echo chambers, gatekeepers, and the price of bipartisanship. In: Proceedings of the World Wide Web Conference. 2018, 913–922 https://doi.org/10.1145/3178876.3186139
38
N DiFonzo. Ferreting facts or fashioning fallacies? factors in rumor accuracy. Social and Personality Psychology Compass, 2010, 4(11): 1124–1137 https://doi.org/10.1111/j.1751-9004.2010.00321.x
C F Chiang, B Knight. Media bias and influence: evidence from newspaper endorsements. The Review of Economic Studies, 2011, 78(3): 795–820 https://doi.org/10.1093/restud/rdq037
K H Jamieson, K K Campbell. Interplay of Influence: News, Advertising, Politics and the Internet Age (with InfoTrac). Belmont: Wadsworth Publishing, 2005
43
R Puglisi. Being the new york times: the political behaviour of a newspaper. The BE Journal of Economic Analysis & Policy, 2011, 11(1): 1–48 https://doi.org/10.2202/1935-1682.2025
44
A S Gerber, D Karlan, D Bergan. Does the media matter? a field experiment measuring the effect of newspapers on voting behavior and political opinions. American Economic Journal: Applied Economics, 2009, 1(2): 35–52 https://doi.org/10.1257/app.1.2.35
45
F N Ribeiro, L Henrique, F Benevenuto, A Chakraborty, J Kulshrestha, M Babaei, K P Gummadi. Media bias monitor: quantifying biases of social media news outlets at large-scale. In: Proceedings of the 12th International AAAI Conference on Web and Social Media. 2018, 290–299
46
C Budak, S Goel, J M Rao. Fair and balanced? quantifying media bias through crowdsourced content analysis. Public Opinion Quarterly, 2016, 80(S1): 250–271 https://doi.org/10.1093/poq/nfw007
47
A Bovet, H A Makse. Influence of fake news in twitter during the 2016 US presidential election. Nature Communications, 2019, 10(1): 7–20 https://doi.org/10.1038/s41467-018-07761-2
N DiFonzo, JW Beckstead, N Stupak, K Walders. Validity judgments of rumors heard multiple times: the shape of the truth effect. Social Influence, 2016, 11(1): 22–39 https://doi.org/10.1080/15534510.2015.1137224
50
E W T Ngai, S S C Tao, K K L Moon. Social media research: theories, constructs, and conceptual frameworks. International Journal of Information Management, 2015, 35(1): 33–44 https://doi.org/10.1016/j.ijinfomgt.2014.09.004
51
H Allcott, M Gentzkow. Social media and fake news in the 2016 election. Journal of Economic Perspectives, 2017, 31(2): 211–236 https://doi.org/10.1257/jep.31.2.211
52
N DiFonzo, M J Bourgeois, J Suls, C Homan, et al. Rumor clustering, consensus, and polarization: dynamic social impact and selforganization of hearsay. Journal of Experimental Social Psychology, 2013, 49(3): 378–399 https://doi.org/10.1016/j.jesp.2012.12.010
53
A Guess, J Nagler, J Tucker. Less than you think: prevalence and predictors of fake news dissemination on facebook. Science Advances, 2019, 5(1): eaau4586
54
C Budak. What happened? the spread of fake news publisher content during the 2016 US presidential election. In: Proceedings of the World Wide Web Conference. 2019, 139–150 https://doi.org/10.1145/3308558.3313721
55
R A Poldrack, M J Farah. Progress and challenges in probing the human brain. Nature, 2015, 526(7573): 371–382 https://doi.org/10.1038/nature15692
56
G Csibra, G Gergely. Natural pedagogy as evolutionary adaptation. Philosophical Transactions of the Royal Society B: Biological Sciences, 2011, 366(1567): 1149–1157 https://doi.org/10.1098/rstb.2010.0319
57
J N Cappella, H S Kim, D Albarracín. Selection and transmission processes for information in the emerging media environment: psychological motives and message characteristics. Media Psychology, 2015, 18(3): 396–424 https://doi.org/10.1080/15213269.2014.941112
58
C Scholz, E C Baek, M B O’Donnell, H S Kim, J N Cappella, E B Falk. A neural model of valuation and information virality. Proceedings of the National Academy of Sciences, 2017, 114(11): 2881–2886 https://doi.org/10.1073/pnas.1615259114
59
N O Hodas, R Butner. How a user’s personality influences content engagement in social media. In: Proceedings of International Conference on Social Informatics. 2016, 481–493 https://doi.org/10.1007/978-3-319-47880-7_30
60
E B Falk, S A Morelli, B L Welborn, K Dambacher, M D Lieberman. Creating buzz: the neural correlates of effective message propagation. Psychological Science, 2013, 24(7): 1234–1242 https://doi.org/10.1177/0956797612474670
61
W Hu, K K Singh, F Xiao, J Han, C N Chuah, Y J Lee. Who will share my image?: predicting the content diffusion path in online social networks. In: Proceedings of ACM International Conference on Web Search and Data Mining. 2018, 252–260
62
Q Zhang, Y Gong, J Wu, H Huang, X Huang. Retweet prediction with attention-based deep neural network. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2016, 75–84 https://doi.org/10.1145/2983323.2983809
63
S Lewandowsky, U K Ecker, C M Seifert, N Schwarz, J Cook. Misinformation and its correction: continued influence and successful debiasing. Psychological Science in the Public Interest, 2012, 13(3): 106–131 https://doi.org/10.1177/1529100612451018
C Camerer, G Loewenstein, D Prelec. Neuroeconomics: how neuroscience can inform economics. Journal of Economic Literature, 2005, 43(1): 9–64 https://doi.org/10.1257/0022051053737843
69
R A Poldrack, M J Farah. Progress and challenges in probing the human brain. Nature, 2015, 526(7573): 371–382 https://doi.org/10.1038/nature15692
70
J P Dmochowski, MA Bezdek, B P Abelson, J S Johnson, E H Schumacher, L C Parra. Audience preferences are predicted by temporal reliability of neural processing. Nature Communications, 2014, 5(1): 1–9 https://doi.org/10.1038/ncomms5567
71
E B Falk, E T Berkman, M D Lieberman. From neural responses to population behavior: neural focus group predicts population-level media effects. Psychological Science, 2012, 23(5): 439–445 https://doi.org/10.1177/0956797611434964
72
U Hasson, Y Nir, I Levy, G Fuhrmann, R Malach. Intersubject synchronization of cortical activity during natural vision. Science, 2004, 303(5664): 1634–1640 https://doi.org/10.1126/science.1089506
73
R Adlolphs. Cognitive neuroscience of human social behavior. Nature Reviews Neuroscience, 2003, 4: 165–178 https://doi.org/10.1038/nrn1056
D Kempe, J Kleinberg, É Tardos. Maximizing the spread of influence through a social network. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003, 137–146 https://doi.org/10.1145/956750.956769
77
P Rozin, E B Royzman. Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 2001, 5(4): 296–320 https://doi.org/10.1207/S15327957PSPR0504_2
J J Argo, D W Dahl, A C Morales. Positive consumer contagion: responses to attractive others in a retail context. Journal of Marketing Research, 2008, 45(6): 690–701 https://doi.org/10.1509/jmkr.45.6.690
F Morone, H A Makse. Influence maximization in complex networks through optimal percolation. Nature, 2015, 524(7563): 65–147 https://doi.org/10.1038/nature14604
G Amati, S Angelini, G Gambosi, G Rossi, P Vocca. Influential users in Twitter: detection and evolution analysis. Multimedia Tools and Applications, 2019, 78(3): 3395–3407 https://doi.org/10.1007/s11042-018-6728-4
84
G Amati, S Angelini, F Capri, G Gambosi, G Rossi, P Vocca. Twitter temporal evolution analysis: comparing event and topic driven retweet graphs. IADIS International Journal on Computer Science & Information Systems, 2016, 11(2): 155–162
85
J Qiu, J Tang, H Ma, Y Dong, K Wang, J Tang. Deepinf: social influence prediction with deep learning. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 2110–2119 https://doi.org/10.1145/3219819.3220077
86
J Ugander, L Backstrom, C Marlow, J Kleinberg. Structural diversity in social contagion. Proceedings of the National Academy of Sciences, 2012, 109(16): 5962–5966 https://doi.org/10.1073/pnas.1116502109
87
A D I Kramer, J E Guillory, J T Hancock. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 2014, 111(24): 8788–8790 https://doi.org/10.1073/pnas.1320040111
88
R Abebe, J Kleinberg, D Parkes, C E Tsourakakis. Opinion dynamics with varying susceptibility to persuasion. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 1089–1098 https://doi.org/10.1145/3219819.3219983
89
J Ratkiewicz, M Conover, M Meiss, B Goncalves, S Patil, A Flammini, F Menczer. Truthy: mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th International Conference Companion on World Wide Web. 2011, 249–252 https://doi.org/10.1145/1963192.1963301
90
A Friggeri, L Adamic, D Eckles, J Cheng. Rumor cascades. In: Proceedings of International AAAI Conference on Weblogs and Social Media. 2014, 101–110
91
X Peng, Y Li, P Wang, L Mo, Q Chen. The ugly truth: negative gossip about celebrities and positive gossip about self entertain people in different ways. Social Neuroscience, 2015, 10(3): 320–336 https://doi.org/10.1080/17470919.2014.999162
92
M Granovetter. Threshold models of collective behavior. American Journal of Sociology, 1978, 83(6): 1420–1443 https://doi.org/10.1086/226707
93
D Kempe, J Kleinberg, É Tardos. Influential nodes in a diffusion model for social networks. In: Proceedings of International Colloquium on Automata, Languages, and Programming. 2005, 1127–1138 https://doi.org/10.1007/11523468_91
94
S Chatterjee, E Seneta. Towards consensus: some convergence theorems on repeated averaging. Journal of Applied Probability, 1977, 14(1): 89–97 https://doi.org/10.2307/3213262
95
Y Wang, E Theodorou, A Verma, L Song. Steering opinion dynamics in information diffusion networks. 2016, arXiv preprint arXiv:1603.09021
96
A C R Martins. Continuous opinions and discrete actions in opinion dynamics problems. International Journal of Modern Physics C, 2008, 19(4): 617–624 https://doi.org/10.1142/S0129183108012339
97
Y Yang, J Tang, C W K Leung, Y Sun, Q Chen, J Li, Q Yang. RAIN: social role-aware information diffusion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 367–373
98
C Castillo, M Mendoza, B Poblete. Information credibility on twitter. In: Proceedings of International Conference on World Wide Web. 2011, 675–684 https://doi.org/10.1145/1963405.1963500
99
M Potthast, J Kiesel, K Reinartz, J Bevendorff, B Stein. A stylometric inquiry into hyperpartisan and fake news. 2017, arXiv preprint arXiv:1702.05638 https://doi.org/10.18653/v1/P18-1022
100
X Hu, J Tang, H Gao, H Liu. Social spammer detection with sentiment information. In: Proceedings of IEEE International Conference on Data Mining. 2014, 180–189
101
V Qazvinian, E Rosengren, D R Radev, Q Mei. Rumor has it: identifying misinformation in microblog. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 1589–1599
102
S Kwon, M Cha, K Jung, W Chen, Y Wang. Prominent features of rumor propagation in online social media. In: Proceedings of IEEE International Conference on Data Mining. 2013, 1103–1108 https://doi.org/10.1109/ICDM.2013.61
103
B D Horne, S Adali. This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Proceedings of the 11th International AAAI Conference on Web and Social Media. 2017, 759–766
104
E Tacchini, G Ballarin, M L Della Vedova, S Moret, L de Alfaro. Some like it hoax: automated fake news detection in social networks. 2017, arXiv preprint arXiv:1704.07506
105
J Ma, W Gao, Z Wei, Y Lu, K F Wong. Detect rumors using time series of social context information on microblogging websites. In: Proceedings of ACM International on Conference on Information and Knowledge Management. 2015, 1751–1754
106
Z Jin, J Cao, Y Zhang, J Luo. News verification by exploiting conflicting social viewpoints in microblogs. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016, 2972–2978
107
S Yang, K Shu, S Wang, R Gu, F Wu, H Liu. Unsupervised fake news detection on social media: a generative approach. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 5644–5651 https://doi.org/10.1609/aaai.v33i01.33015644
108
M Gupta, P Zhao, J Han. Evaluating event credibility on twitter. In: Proceedings of the SIAMInternational Conference on DataMining, Society for Industrial and Applied Mathematics. 2012, 153–164 https://doi.org/10.1137/1.9781611972825.14
109
Z Jin, J Cao, Y G Jiang, Y Zhang. News credibility evaluation on microblog with a hierarchical propagation model. In: Proceedings of IEEE International Conference on Data Mining. 2014, 230–239 https://doi.org/10.1109/ICDM.2014.91
110
K Shu, S Wang, H Liu. Understanding user profiles on social media for fake news detection. In: Proceedings of IEEE Conference on Multimedia Information Processing and Retrieval. 2018, 430–435 https://doi.org/10.1109/MIPR.2018.00092
111
K Wu, S Yang, K Q Zhu. False rumors detection on sinaweibo by propagation structures. In: Proceedings of the 31st IEEE International Conference on Data Engineering. 2015, 651–662
112
F Jin, E Dougherty, P Saraf, Y Cao, N Ramakrishnan. Epidemiological modeling of news and rumors on twitter. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis. 2013, 1–9 https://doi.org/10.1145/2501025.2501027
113
Y Liu, S Xu. Detecting rumors through modeling information propagation networks in a social media environment. IEEE Transactions on Computational Social Systems, 2016, 3(2): 46–62 https://doi.org/10.1109/TCSS.2016.2612980
114
J Kim, D Kim, A Oh. Homogeneity-based transmissive process to model true and false news in social networks. In: Proceedings of ACM International Conference on Web Search and Data Mining. 2019, 348–356 https://doi.org/10.1145/3289600.3291009
115
J Ma, W Gao, K F Wong. Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017, 708–717
116
F Yu, Q Liu, S Wu, L Wang, T Tan. A convolutional approach for misinformation identification. In: Proceedings of International Joint Conference on Artificial Intelligence. 2017, 3901–3907
117
J Ma, W Gao, P Mitra, S Kwon, B J Jansen, K F Wong, M Cha. Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of International Joint Conference on Artificial Intelligence. 2016, 3818–3824
118
L Li, G Cai, N Chen. A rumor events detection method based on deep bidirectional GRU neural network. In: Proceedings of the 3rd IEEE International Conference on Image, Vision and Computing. 2018, 755–759
119
Y Liu, Y F B Wu. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018, 354–361
120
N Ruchansky, S Seo, Y Liu. CSI: a hybrid deep model for fake news detection. In: Proceedings of ACM on Conference on Information and Knowledge Management. 2017, 797–806
121
Z Jin, J Cao, H Guo, Y Zhang, J Luo. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of ACM International Conference on Multimedia. 2017, 795–816 https://doi.org/10.1145/3123266.3123454
122
Q Liu, F Yu, S Wu, L Wang. Mining significant microblogs for misinformation identification: an attention-based approach. ACM Transactions on Intelligent Systems and Technology, 2018, 9(5): 50–67 https://doi.org/10.1145/3173458
123
H Guo, J Cao, Y Zhang, J Guo, J Li. Rumor detection with hierarchical social attention network. In: Proceedings of ACM International Conference on Information and Knowledge Management. 2018, 943–951
124
K Popat, S Mukherjee, A Yates, G Weikum. DeClarE: debunking fake news and false claims using evidence-aware deep learning. 2018, arXiv preprint arXiv:1809.06416 https://doi.org/10.18653/v1/D18-1003
125
E Ferrara, O Varol, C Davis, F Menczer, A Flammini. The rise of social bots. Communications of the ACM, 2016, 59(7): 96–104 https://doi.org/10.1145/2818717
126
C A de Lima Salge, N Berente. Is that social bot behaving unethically?. Communications of the ACM, 2017, 60(9): 29–31 https://doi.org/10.1145/3126492
127
Z Chu, S Gianvecchio, H Wang, S Jajodia. Detecting automation of twitter accounts: are you a human, bot, or cyborg?. IEEE Transactions on Dependable and Secure Computing, 2012, 9(6): 811–824 https://doi.org/10.1109/TDSC.2012.75
S Yu, G Gu, A Barnawi, S Guo, I Stojmenovic. Malware propagation in large-scale networks. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(1): 170–179 https://doi.org/10.1109/TKDE.2014.2320725
130
Y Boshmaf, I Muslukhov, K Beznosov, M Ripeanu. The socialbot network: when bots socialize for fame and money. In: Proceedings of the 27th Annual Computer Security Applications Conference. 2011, 93–102 https://doi.org/10.1145/2076732.2076746
131
S Haustein, T D Bowman, K Holmberg, A Tsou, C R Sugimoto, V Larivière. Tweets as impact indicators: examining the implications of automated “bot” accounts on twitter. Journal of the Association for Information Science and Technology, 2016, 67(1): 232–238 https://doi.org/10.1002/asi.23456
132
Z Gilani, R Farahbakhsh, G Tyson, L Wang, J Crowcroft. An in-depth characterisation of bots and humans on Twitter. 2017, arXiv preprint arXiv:1704.01508 https://doi.org/10.1145/3110025.3110090
133
S Yu, S Guo, I Stojmenovic. Fool me if you can: mimicking attacks and anti-attacks in cyberspace. IEEE Transactions on Computers, 2013, 64(1): 139–151 https://doi.org/10.1109/TC.2013.191
134
O Varol, E Ferrara, C A Davis, F Menczer, A Flammini. Online humanbot interactions: detection, estimation, and characterization. In: Proceedings of the 11th International AAAI Conference on Web and Social Media. 2017, 280–289
135
K Thomas, C Grier, J Ma, V Paxson, D Song. Design and evaluation of a real-time url spam filtering service. In: Proceedings of IEEE Symposium on Security and Privacy. 2011, 447–462 https://doi.org/10.1109/SP.2011.25
136
M Egele, G Stringhini, C Kruegel, G Vigna. Towards detecting compromised accounts on social networks. IEEE Transactions on Dependable and Secure Computing, 2015, 14(4): 447–460 https://doi.org/10.1109/TDSC.2015.2479616
137
S Kudugunta, E Ferrara. Deep neural networks for bot detection. Information Sciences, 2018, 467: 312–322
138
H Gao, Y Yang, K Bu, Y Chen, D Downey, K Lee, A Choudhary. Spam ain’t as diverse as it seems: throttling OSN spam with templates underneath. In: Proceedings of the 30th Annual Computer Security Applications Conference. 2014, 76–85 https://doi.org/10.1145/2664243.2664251
139
J Messias, L Schmidt, R A R D Oliveira, F B D Souza. You followed my bot! transforming robots into influential users in twitter. Peer-Reviewed Journal on the Internet, 2013, 18(7–1): 1–14 https://doi.org/10.5210/fm.v18i7.4217
140
N Abokhodair, D Yoo, D M McDonald. Dissecting a social botnet: growth, content and influence in twitter. In: Proceedings of ACM Conference on Computer Supported Cooperative Work& Social Computing. 2015, 839–851 https://doi.org/10.1145/2675133.2675208
141
C Freitas, F Benevenuto, S Ghosh, A Veloso. Reverse engineering socialbot infiltration strategies in twitter. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2015, 25–32 https://doi.org/10.1145/2808797.2809292
142
J Guixeres, E Bigné, J M AusñíAzofra, M Alcañiz Raya, A Colomer Granero, F Fuentes Hurtado, V Naranjo Ornedo. Consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising. Frontiers in Psychology, 2017, 8: 1808–1821 https://doi.org/10.3389/fpsyg.2017.01808
143
B Yılmaz, S Korkmaz, D B Arslan, E Güngör, M H Asyalı. Like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Computer Methods and Programs in Biomedicine, 2014, 113(2): 705–713
144
S Lewandowsky, U K H Ecker, J Cook. Beyond misinformation: understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition, 2017, 6(4): 353–369 https://doi.org/10.1016/j.jarmac.2017.07.008
145
I Arapakis, M Barreda-Angeles, A Pereda-Baños. Interest as a proxy of engagement in news reading: spectral and entropy analyses of EEG activity patterns. IEEE Transactions on Affective Computing, 2017, 10(1): 100–114 https://doi.org/10.1109/TAFFC.2017.2682089
146
T Chen, X Li, H Yin, J Zhang. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2018, 40–52 https://doi.org/10.1007/978-3-030-04503-6_4
147
K Shu, L Cui, S Wang, D Lee, H Liu. dEFEND: explainable fake news detection. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 395–405
148
M H Gad-Elrab, D Stepanova, J Urbani, G Weikum. ExFaKT: a framework for explaining facts over knowledge graphs and text. In: Proceedings of ACM International Conference onWeb Search and Data Mining. 2019, 87–95 https://doi.org/10.1145/3289600.3290996
149
A T Nguyen, A Kharosekar, M Lease, B Wallace. An interpretable joint graphical model for fact-checking from crowds. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018, 1511–1518
150
M Du, N Liu, X Hu. Techniques for interpretable machine learning. Communications of the ACM, 2019, 63(1): 68–77 https://doi.org/10.1145/3359786
151
W J Murdoch, C Singh, K Kumbier, R Abbasi-Asl, B Yu. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 2019, 116(44): 22071–22080 https://doi.org/10.1073/pnas.1900654116
152
N Vo, K Lee. The rise of guardians: fact-checking url recommendation to combat fake news. In: Proceedings of ACM SIGIR Conference on Research & Development in Information Retrieval. 2018, 275–284
153
J Kim, B Tabibian, A Oh, B Schölkopf, M Gomez-Rodriguez. Leveraging the crowd to detect and reduce the spread of fake news and misinformation. In: Proceedings of ACM International Conference on Web Search and Data Mining. 2018, 324–332 https://doi.org/10.1145/3159652.3159734
154
S D Bhattacharjee, A Talukder, B V Balantrapu. Active learning based news veracity detection with feature weighting and deep-shallow fusion. In: Proceedings of IEEE International Conference on Big Data. 2017, 556–565 https://doi.org/10.1109/BigData.2017.8257971
155
J Cao, J Guo, X Li, Z Jin, H Guo, J Li. Automatic rumor detection on microblogs: a survey. 2018, arXiv preprint arXiv:1807.03505
156
Z Zhao, P Resnick, i Q Me. Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of International Conference on World Wide Web. 2015, 1395–1405 https://doi.org/10.1145/2736277.2741637
157
J Sampson, F Morstatter, L Wu, H Liu. Leveraging the implicit structure within social media for emergent rumor detection. In: Proceedings of ACM International on Conference on Information and Knowledge Management. 2016, 2377–2382
158
X Liu, A Nourbakhsh, Q Li, R Fang, S Shah. Real-time rumor debunking on twitter. In: Proceedings of ACM International on Conference on Information and Knowledge Management. 2015, 1867–1870 https://doi.org/10.1145/2806416.2806651
159
F Qian, C Gong, K Sharma, Y Liu. Neural user response generator: fake news detection with collective user intelligence. In: Proceedings of International Joint Conference on Artificial Intelligence. 2018, 3834–3840 https://doi.org/10.24963/ijcai.2018/533
160
S Tschiatschek, A Singla, M Gomez Rodriguez, A Merchant, A Krause. Fake news detection in social networks via crowd signals. In: Proceedings of the Web Conference. 2018, 517–524 https://doi.org/10.1145/3184558.3188722
161
C Tan, F Sun, T Kong, W Zhang, C Yang, C Liu. A survey on deep transfer learning. In: Proceedings of International Conference on Artificial Neural Networks. 2018, 270–279 https://doi.org/10.1007/978-3-030-01424-7_27
162
Z Li, Y Wei, Y Zhang, Q Yang. Hierarchical attention transfer network for cross-domain sentiment classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018, 5852–5859
163
W Wang, V W Zheng, H Yu, C Miao. A survey of zero-shot learning: settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 1–37 https://doi.org/10.1145/3293318
164
R Socher, M Ganjoo, C D Manning, A Ng. Zero-shot learning through cross-modal transfer. In: Proceedings of Advances in Neural Information Processing Systems. 2013, 935–943
165
H Yao, Y Liu, Y Wei, X Tang, Z Li. Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: Proceedings of The World Wide Web Conference. 2019, 2181–2191 https://doi.org/10.1145/3308558.3313577
166
C Finn, P Abbeel, S Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of International Conference on Machine Learning-Volume 70. 2017, 1126–1135
167
A Santoro, S Bartunov, M Botvinick, D Wierstra, T Lillicrap. Metalearning with memory-augmented neural networks. In: Proceedings of International Conference on Machine Learning. 2016, 1842–1850
168
A L Ginsca, A Popescu, M Lupu. Credibility in information retrieval. Foundations and Trends in Information Retrieval, 2015, 9(5): 355–475 https://doi.org/10.1561/1500000046
169
B Shi, T Weninger. Fact checking in heterogeneous information networks. In: Proceedings of the 25th International Conference Companion on World Wide Web. 2016, 101–102 https://doi.org/10.1145/2872518.2889354
170
B Nyhan, J Reifler. When corrections fail: the persistence of political misperceptions. Political Behavior, 2010, 32(2): 303–330 https://doi.org/10.1007/s11109-010-9112-2
171
P Bordia, N DiFonzo, R Haines, E Chaseling. Rumors denials as persuasive messages: effects of personal relevance, source, and message characteristics. Journal of Applied Social Psychology, 2005, 35(6): 1301–1331 https://doi.org/10.1111/j.1559-1816.2005.tb02172.x
172
Y Tanaka, Y Sakamoto, H Honda. The impact of posting urls in disasterrelated tweets on rumor spreading behavior. In: Proceedings of the 47th Hawaii International Conference on System Sciences. 2014, 520–529 https://doi.org/10.1109/HICSS.2014.72
173
P Ozturk, H Li, Y Sakamoto. Combating rumor spread on social media: the effectiveness of refutation and warning. In: Proceedings of the 48th Hawaii International Conference on System Sciences. 2015, 2406–2414 https://doi.org/10.1109/HICSS.2015.288
174
A Alemanno. How to counter fake news? a taxonomy of anti-fake news approaches. European Journal of Risk Regulation, 2018, 9(1): 1–5 https://doi.org/10.1017/err.2018.12
Y T Chang, H Yu, H P Lu. Persuasive messages, popularity cohesion, and message diffusion in social media marketing. Journal of Business Research, 2015, 68(4): 777–782 https://doi.org/10.1016/j.jbusres.2014.11.027
177
WM Huang, L J Zhang, X J Xu, X Fu. Contagion on complex networks with persuasion. Scientific Reports, 2016, 6: 23766–23773 https://doi.org/10.1038/srep23766