Detecting Social Media Manipulation in Low-Resource Languages

Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data-the norm when dealing with detecting malicious activity in online platforms

Coordinated amplification, coordinated inauthentic behaviour, orchestrated campaigns:A systematic literature review of coordinated inauthentic content on online social networks

The internet and online social networks have resulted in dramatic changes in the information landscape. Pessimistic views fear that networks and algorithms can limit exposure to various content by exposing users to pre-existing beliefs. In this respect, coordinated campaigns can amplify these individuals' voices above the crowd, capable of hijacking conversations, influencing other users and manipulating content dissemination. Through a systematic literature review, this chapter locates and synthesises related research on coordinated activities to (i) describe the state of this field by identifying the patterns and trends in the conceptual and methodological approaches, topics and practices; and (ii) shed light on potentially essential gaps in the field and suggest recommendations for future research. Findings show an evolution of the approaches used to detect coordinated activities. While bot detection was the focus in the early years, more recent research focused on using advanced computational methods based on training datasets or identifying coordinated campaigns by timely and similar content. Due to the data availability, Twitter is the most studied online social network, although studies have shown that coordinated activities can be found on other platforms. We conclude by discussing the implications of current approaches and outlining an agenda for future research

PENERAPAN METODE SUPPORT VECTOR MACHINE UNTUK MENEMUKAN BOT SPAMMER PADA TWITTER

Twitter merupakan jejaring sosial yang paling cepat berkembang dan memiliki popularitas yang tinggi. Twitter merupakan suatu cara baru bagi orang-orang untuk menemukan, berbagi dan membaca berita dan informasi di internet melalui jaringan sosial. Popularitas dan kemudahan akses Twitter memicu munculnya program otomatisasi yang biasa dikenal dengan sebutan bot. bot memiliki pengaruh positif dan negatif pada media sosial Twitter. Dampak positifnya seperti mengirim berita dan memperbarui feed, sementara dampak buruknya adalah menyebarkan spam atau konten berbahaya. Oleh karena dampak yang ditimbulkannya banyak penelitian yang mengklasifikasikan dan mengenali karakteristik bot. Pada penelitian ini akan dilakukan klasifikasi bot berdasarkan perilaku untuk menentukan pengguna tersebut bot spammer atau legitimate user. Penelitian ini dilakukan dengan data tweet hashtag #pilpres2019, #PemiluJujurAdil, dan #AuditForensikKPU yang didapat menggunakan API Twitter. Bersumber pada data yang ada dilakukan perhitungan dan pengujian dengan menggunakan metode Support Vector Machine. Hasil pengujian memiliki nilai rasio akurasi tertinggi pada dasaset I yaitu 66,14%

KLASIFIKASI BOT MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBOR PADA MEDIA SOSIAL TWITTER

Twitter merupakan media sosial online untuk berkomunikasi dengan menggunakan posting-an teks yang di kenal dengan nama tweet. Perkembangan Twitter membuat banyak akun otomatis yang dikendalikan oleh program komputer (bot) bermunculan. Bot mempunyai dampak positif dan negatif terhadap Twitter, oleh karena itu banyak penelitian melakukan klasifikasi untuk mengetahui akun tersebut bot atau legitimate user. Pada penelitian ini dilakukan klasifikasi terhadap bot yang ditinjau dari atribut dasar pengguna Twitter, yaitu jumlah following, jumlah follower, account reputation, usia akun, source tweet, nilai rataan selang waktu antar tweet, jumah mention, jumlah hashtag, dan jumlah URL. Penelitian ini menggunakan data tweet dengan hashtag #Pilpres2019, #PemiluJujurAdil dan #AuditForensikKPU yang di-crawling menggunakan Twitter API. Dari hasil crawling tersebut terkumpul 1000 akun. Kemudian setelah melewati proses cleaning, data yang terkumpul menjadi 800 akun. Berdasarkan data yang diperoleh kemudian dilakukan perhitungan menggunakan model klasifikasi Modified K-Nearest Neighbor. Pengujian dilakukan dengan metode confusion matrix untuk mengetahui tingkat akurasi dari metode. Dari hasil pengujian, akurasi tertinggi terletak pada k=5, k=7 dan k=9 dengan nilai akurasi 86,3%. Metode Modified K-Nearst Neighbor berhasil diterapkan untuk mengklasifikasikan akun bot pada media sosial Twitter dengan tingkat akurasi yang cukup tinggi

Promotional Campaigns in the Era of Social Platforms

The rise of social media has facilitated the diffusion of information to more easily reach millions of users. While some users connect with friends and organically share information and opinions on social media, others have exploited these platforms to gain influence and profit through promotional campaigns and advertising. The existence of promotional campaigns contributes to the spread of misleading information, spam, and fake news. Thus, these campaigns affect the trustworthiness and reliability of social media and render it as a crowd advertising platform. This dissertation studies the existence of promotional campaigns in social media and explores different ways users and bots (i.e. automated accounts) engage in such campaigns. In this dissertation, we design a suite of detection, ranking, and mining techniques. We study user-generated reviews in online e-commerce sites, such as Google Play, to extract campaigns. We identify cooperating sets of bots and classify their interactions in social networks such as Twitter, and rank the bots based on the degree of their malevolence. Our study shows that modern online social interactions are largely modulated by promotional campaigns such as political campaigns, advertisement campaigns, and incentive-driven campaigns. We measure how these campaigns can potentially impact information consumption of millions of social media users