Automatic Detection of Online Jihadist Hate Speech

We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.Comment: 31 page

A Quantitative Approach to Understanding Online Antisemitism

A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand these troubling trends. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million posts and images from alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the "Unite the Right" rally in Charlottesville. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic "Happy Merchant" meme, and in particular how these fringe communities influence its propagation to more mainstream communities like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of hate online.Comment: To appear at the 14th International AAAI Conference on Web and Social Media (ICWSM 2020). Please cite accordingl

#ParisAttack : Making sense of a terrorist attack in Twitter

Pariisissa 13. marraskuuta 2015 tapahtui seitsemän terrori-iskun sarja, jossa uhriluku nousi 129 henkeen ja loukkaantuneita oli noin 352. Terrori-isku sai paljon mediahuomiota osakseen ja sen takana oli terroristijärjestö ISIS (The Islamic State of Iraq and Syria). Keskustelu eri sosiaalisen median kanavissa oli vilkasta iskujen jälkeen. Tämä Pro gradu –tutkielma keskittyy terrori-iskun jälkeiseen keskusteluun ja ihmisten ensireaktioihin Twitterissä. Koska aikaisempaa tutkimusta tämän tyyppisen kriisin ensireaktioista on hyvin rajallisesti, data, jota tässä tutkielmassa käsitellään, rajoittuu tviitteihin, jotka lähetettiin neljän päivän sisällä iskuista. Tutkimuksen tavoitteena oli mallintaa millaisia ensireaktioita ihmisillä oli Islamin nimeen tehtyjen terroristi-iskujen jälkeen, mitkä teemat tviiteissä nousivat esiin, mihin tarkoitukseen Twitteriä käytettiin ja minkälainen rooli uskonnolla oli ihmisten järkeistämisprosessissa (sense-making). Tämän tutkielman tutkimusstrategiana on tapaustutkimus. Data kerättiin Twitteristä Pulsar nimisellä työkalulla. Datan rajaamiseksi käytettiin aihetunnisteita #parisattack, #parisshooting ja #paristerror sekä ajallista ja kieleen liittyvää rajaamista. Tiedon analysoinnin metodina käytettiin sisältöanalyysia. Tutkimuksen perusteella, Twitteriä käytettiin laajasti Pariisin terrori-iskujen jälkeen ja tiedon jakamisen tarve korostui Twitterin ensireaktioissa. Muita syitä tviittaamiseen olivat mielipiteiden jakaminen tai hallitsevan tunteen ilmaiseminen. Uskonto esiintyi suhteellisen pienessä osassa tviittejä. Nämä löydökset tukevat aikaisempaa tutkimusta tiedon saamisen tärkeydestä alkuvaiheessa kriisitilanteen tapahduttua, ja siten selittää pientä uskontoa käsittelevien tviittien osuutta. Kun dataa tarkasteltiin vain uskontoaiheisten tviittien osalta, mielipiteiden osuus korostui. Suuri osa näistä tviiteistä pyrki edistämään rauhanomaista yhteisymmärrystä (concensus) pääviesteinään se, että Muslimeja, Islamia tai uskontoa ei ole syyttäminen terrori-iskuista. Toisaalta noin neljännes tviiteistä piti edellä mainittuja syyllisenä iskuihin ja pyrkivät aiheuttamaan vastakkainasettelua (confrontation). Nämä löydökset viittaavat siihen, että uskonto jakoi mielipiteitä ja siitä etsittiin syitä terrori-iskuihin. Tämän tutkimuksen mukaan uskonto oli osa ihmisten järkeistämisprosessia uskontoaiheisten tviittien pienestä lukumäärästä huolimatta

An analysis of emotion-exchange motifs in multiplex networks during emergency events

In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchangemotifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension

How can Big Data from Social Media be used in Emergency Management? A case study of Twitter during the Paris attacks

Postponed access: the file will be accessible after 2019-06-11Over the past years, social media have impacted emergency management and disaster response in numerous ways. The access to live, continuous updates from the public brings new opportunities when it comes to detecing, coordinating and aiding in an emergency situation. The thesis present a research of social media during an emergency situation. The goal of the study is to discover how data from social media can be used for emergency management and determine if existing analysis services can be proven useful for the same occasion. To achieve the goal, a dataset from Twitter during the Paris attacks 2015 was collected. The dataset was analyzed using three different analysis tools; IBM Watson Discovery service, Microsoft Azure Text Analytics and an own developed Keyword Frequency Script. The results indicate that data from social media can be used for emergency management, in form of detecting and providing important information. Additional testing with larger datasets is needed to fully demonstrate the usefulness, in addition to interviews with emergency responders and social media users.Masteroppgave i informasjonsvitenskapINFO39

Understanding Bots on Social Media - An Application in Disaster Response

abstract: Social media has become a primary platform for real-time information sharing among users. News on social media spreads faster than traditional outlets and millions of users turn to this platform to receive the latest updates on major events especially disasters. Social media bridges the gap between the people who are affected by disasters, volunteers who offer contributions, and first responders. On the other hand, social media is a fertile ground for malicious users who purposefully disturb the relief processes facilitated on social media. These malicious users take advantage of social bots to overrun social media posts with fake images, rumors, and false information. This process causes distress and prevents actionable information from reaching the affected people. Social bots are automated accounts that are controlled by a malicious user and these bots have become prevalent on social media in recent years. In spite of existing efforts towards understanding and removing bots on social media, there are at least two drawbacks associated with the current bot detection algorithms: general-purpose bot detection methods are designed to be conservative and not label a user as a bot unless the algorithm is highly confident and they overlook the effect of users who are manipulated by bots and (unintentionally) spread their content. This study is trifold. First, I design a Machine Learning model that uses content and context of social media posts to detect actionable ones among them; it specifically focuses on tweets in which people ask for help after major disasters. Second, I focus on bots who can be a facilitator of malicious content spreading during disasters. I propose two methods for detecting bots on social media with a focus on the recall of the detection. Third, I study the characteristics of users who spread the content of malicious actors. These features have the potential to improve methods that detect malicious content such as fake news.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Spatiotemporal Variation in Emotional Responses to 2017 Terrorist Attacks in London Using Twitter Data

Terrorist attacks have a significant impact on human lives. This study examined emotional responses after the terrorist attacks in London in March and June of 2017, respectively. This research extracted tweets related to the two attacks by developing a Python tool interacting with the Twitter Application Program Interface (API). The tweets were analyzed for its negative emotion expression such as sadness. This study then analyzed these negative tweets using the space-time permutation model in SatScan and assessed their variation in space and time. Results suggested two significant clusters of negative tweets after the first attack. These clusters located in the metropolitan area of London and between Manchester and Liverpool within ten days of the attack. The findings may contribute to quick surveillance of emotional responses on the Twitter users

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann, T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA. DOI: https://doi.org/10.1145/3173574.317413