本文選題：網(wǎng)絡(luò)水軍 + 水軍識(shí)別��； 參考：《通信學(xué)報(bào)》2017年01期

【摘要】：為了能夠有效地識(shí)別水軍,在以往相關(guān)研究基礎(chǔ)上,設(shè)置粉絲關(guān)注比、平均發(fā)布微博數(shù)、互相關(guān)注數(shù)、綜合質(zhì)量評(píng)價(jià)、收藏?cái)?shù)和陽(yáng)光信用這6個(gè)特征屬性來設(shè)計(jì)微博水軍識(shí)別分類器,并基于貝葉斯模型和遺傳智能優(yōu)化算法實(shí)現(xiàn)了水軍識(shí)別算法。利用新浪微博真實(shí)數(shù)據(jù)對(duì)算法性能進(jìn)行了驗(yàn)證,實(shí)驗(yàn)結(jié)果表明,提出的貝葉斯水軍識(shí)別算法能夠在不犧牲非水軍識(shí)別率的情況下,保證水軍識(shí)別的準(zhǔn)確率,而且提出的閾值優(yōu)化算法能顯著提升水軍識(shí)別的準(zhǔn)確率。
[Abstract]:In order to be able to identify the navy effectively, based on the previous relevant research, we set the fan concern ratio, average Weibo number, mutual concern number, comprehensive quality evaluation. The six characteristic attributes of collection number and sunshine credit are used to design Weibo water army recognition classifier, and based on Bayesian model and genetic intelligence optimization algorithm, the recognition algorithm of water army is realized. The performance of the algorithm is verified by using the real data of Sina Weibo. The experimental results show that the proposed Bayesian recognition algorithm can ensure the accuracy of water army recognition without sacrificing the recognition rate of non-water army. Moreover, the proposed threshold optimization algorithm can significantly improve the accuracy of water army recognition.
【作者單位】： 中央財(cái)經(jīng)大學(xué)信息學(xué)院;電子科技大學(xué)網(wǎng)絡(luò)與數(shù)據(jù)安全四川省重點(diǎn)實(shí)驗(yàn)室;新疆財(cái)經(jīng)大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(No.61602536,No.61273293,No.61309029) 北京市社會(huì)科學(xué)重點(diǎn)基金資助項(xiàng)目(No.16YJA001) 網(wǎng)絡(luò)與數(shù)據(jù)安全四川省重點(diǎn)實(shí)驗(yàn)室開放課題基金資助項(xiàng)目(No.NDSMS201605) 中央財(cái)經(jīng)大學(xué)學(xué)科建設(shè)基金資助項(xiàng)目~~
【分類號(hào)】：TP18

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