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    string(9897) "<!-- wp:acf/ihr-story-introblock {"id":"block_621cfa87b2eab","name":"acf/ihr-story-introblock","data":{"text":"At the time of this interview, Raashi Saxena was the global project coordinator of \u003ca href=\u0022https://hatebase.org/\u0022\u003eHatebase\u003c/a\u003e at the \u003ca href=\u0022https://thesentinelproject.org/\u0022\u003eSentinel Project\u003c/a\u003e, working with a global community of contributors via the \u003ca href=\u0022https://hatebase.org/citizen_linguist\u0022\u003eCitizen Linguist Lab\u003c/a\u003e. She is also a contributor to several other civic and internet governance communities, including as co-chair of \u003ca href=\u0022https://internetrightsandprinciples.org/irp-coalition-chairs-steering-committee/\u0022\u003eThe Internet Rights and Principles Coalition\u003c/a\u003e.","_text":"field_621cfe1f2ceab","font_color":"black","_font_color":"field_62044418c7d75","background_color":"grey","_background_color":"field_620441bac7d74","dropshadow":"none","_dropshadow":"field_6267f7f921c52","classname":"","_classname":"field_6204244253a3f","observer_id":"","_observer_id":"field_6204245b53a40","anchor_button_text":"","_anchor_button_text":"field_62b9b0d090a10","anchor_id":"","_anchor_id":"field_6204242753a3e"},"align":"","mode":"edit"} /-->

<!-- wp:acf/ihr-textblocks {"id":"block_62197bcf08855","name":"acf/ihr-textblocks","data":{"hide_page_title":"0","_hide_page_title":"field_62d294c9f62e2","textblocks_0_text":"\u003ci\u003eThis is an extended cut of the interview from \u003c/i\u003e\u003ca href=\u0022https://2022.internethealthreport.org/episodes/the-truth-is-out-there/\u0022\u003e\u003ci\u003eThe Truth is Out There\u003c/i\u003e\u003c/a\u003e\u003ci\u003e that has been edited for ease of reading.\u003c/i\u003e","_textblocks_0_text":"field_621976bef3f4d","textblocks_0_bottom_space":"0","_textblocks_0_bottom_space":"field_621977a9f3f4f","textblocks_1_text":"What is the link between hate speech and disinformation, especially around elections?","_textblocks_1_text":"field_621e38d641532","textblocks_1_bottom_space":"0","_textblocks_1_bottom_space":"field_62d29698c68a1","textblocks_2_text":"There is an overlap. You can have hateful content in misinformation. And a lot of hate speech can also be seen as misinformation. So these two phenomena are very closely related. We have this analogy where we say, “Hate speech loads the gun, but misinformation pulls the trigger.” And what we mean by that, is that when you have a rumor that aligns with a pre-existing hostility, this makes violence more likely.\r\n\r\nAt the Sentinel Project, we’re particularly interested in understanding how online hate speech can lead to offline violence. We’re also interested in how offline atrocities can affect situations online. So we examine hate speech from a very contextual perspective. Hate speech in itself might not contribute to offline violence, but it sets a tone of background hostility towards a particular community. And then rumors in the form of malignant and harmful information circulate and can lead to election violence.","_textblocks_2_text":"field_621976bef3f4d","textblocks_2_bottom_space":"0","_textblocks_2_bottom_space":"field_621977a9f3f4f","textblocks_3_text":"\u003cb\u003eWhat is Hatebase?\u003c/b\u003e","_textblocks_3_text":"field_621e38d641532","textblocks_3_bottom_space":"0","_textblocks_3_bottom_space":"field_62d29698c68a1","textblocks_4_text":"\u003ca href=\u0022http://hatebase.org/\u0022\u003eHatebase\u003c/a\u003e is a monitoring tool that can be used to analyze hate speech terms across the world. It’s a repository of hate speech in 95+ languages in 175 countries. We analyze publicly available conversations, a lot of which are on social media platforms. It was built to be collaborative, so that companies, government agencies, NGOs, communities and research organizations can use it to help moderate online conversations and potentially gain insights to predict regional or localized violence.\r\n\r\nGetting more regional and linguistic inputs is a huge challenge as we're a very small team. The environments we work in are usually high friction, with civil wars or conflicts. Our data supports \u003ca href=\u0022https://hatebase.org/citations\u0022\u003ea lot of research\u003c/a\u003e, and we have relationships with around 350 universities around the world. We have done misinformation management work to mitigate offline violence in Kenya, South Sudan, and Congo, and we’ve also done work in Sri Lanka and Myanmar.","_textblocks_4_text":"field_621976bef3f4d","textblocks_4_bottom_space":"0","_textblocks_4_bottom_space":"field_621977a9f3f4f","textblocks_5_text":"\u003cb\u003eHow do you get input in so many languages? \u003c/b\u003e","_textblocks_5_text":"field_621e38d641532","textblocks_5_bottom_space":"0","_textblocks_5_bottom_space":"field_62d29698c68a1","textblocks_6_text":"We have something called the \u003ca href=\u0022https://hatebase.org/citizen_linguist\u0022\u003eCitizen Linguist Lab\u003c/a\u003e which is a form of crowdsourcing for anyone that wants to amplify and augment our database. You don't have to be an expert to contribute. You can input terms with their contexts, categorizing them based on gender, sexual orientation, ethnicity, and class. You can also provide a citation or more information about the term's origin. Contributors can also offer an assessment of the ‘offensiveness’ of a particular term. This lets us crowdsource sentiment analysis.\r\n\r\nTerms change over time, or may only be offensive to a particular community or in one part of the world. The offensiveness rating helps us to understand. Communities are an integral part of any project to learn who is impacted by hate speech and misinformation. They're also essential to help us direct research towards facets that matter the most by assisting with data collection, providing linguistic nuances, and highlighting different social and cultural considerations.","_textblocks_6_text":"field_621976bef3f4d","textblocks_6_bottom_space":"0","_textblocks_6_bottom_space":"field_621977a9f3f4f","textblocks_7_text":"What are your thoughts on content moderation?","_textblocks_7_text":"field_621e38d641532","textblocks_7_bottom_space":"0","_textblocks_7_bottom_space":"field_62d29698c68a1","textblocks_8_text":"Big tech puts emphasis on stamping out content that violates their rules, but we’ve found this is mostly a losing proposition. Rather than doing reactive work, we believe there should be more effort placed on educating users on how to detect misinformation and providing impartial guidance on how to verify information. There are things that fall within a gray area that aren’t necessarily hate speech, or get lost in translation.\r\n\r\nSocial media companies use automation for moderation at a base level, but when it gets escalated to a secondary level, they might not have the local expertise necessary. One fix is to hire content moderators that understand local contexts. I think educating users on how to detect misinformation is better. Companies can delete content, but censorship seems to have a way of reinforcing the beliefs of people involved in spreading propaganda. You’re essentially removing the hateful term, but not the intent. We also see speech labeled as hate speech, when it’s just disliked by a particular group. So these systems can also be used to silence people.\r\n\r\nWe don't believe in policing speech. With Hatebase, we monitor speech to understand it for research purposes. Data alone cannot reveal the full picture. We need local guidance. It's a fruitful path that more technology companies and platforms should pursue. AI is not a magic wand that will automatically wash away hate speech.","_textblocks_8_text":"field_621976bef3f4d","textblocks_8_bottom_space":"0","_textblocks_8_bottom_space":"field_621977a9f3f4f","textblocks_9_text":"What role is there for automation in Hatebase?","_textblocks_9_text":"field_621e38d641532","textblocks_9_bottom_space":"0","_textblocks_9_bottom_space":"field_62d29698c68a1","textblocks_10_text":"Working on this topic day in and day out, we understand the limitations of AI to identify hate speech, and we are pragmatic about it. One thing we do is collect the metadata of Hatebase terms we detect in public conversations. We call these \u003ca href=\u0022https://hatebase.org/recent_sightings/\u0022\u003e“sightings”\u003c/a\u003e and there have been around a million so far. Soon, we expect to expand the natural language processing (NLP) engine of Hatebase — called HateBrain — so it also analyzes the textual context (ngrams) of terms, plus frequency and proximity patterns. These additional data points will increase Hatebase’s sensitivity to language, and help to expand the dataset.\r\n\r\n\u003ci\u003eMozilla has taken reasonable steps to ensure the accuracy of the statements made during the interview, but the words and opinions presented here are ascribed entirely to the interviewee.\u003c/i\u003e\r\n\r\n\u003cb\u003ePortrait photo of Raashi Saxena is by\u003c/b\u003e\u003ca href=\u0022http://hannahyoon.com\u0022 target=\u0022_blank\u0022 rel=\u0022external noopener noreferrer\u0022 data-wpel-link=\u0022external\u0022\u003e \u003cb\u003eHannah Yoon\u003c/b\u003e\u003c/a\u003e\u003cb\u003e (CC-BY) 2022\u003c/b\u003e","_textblocks_10_text":"field_621976bef3f4d","textblocks_10_bottom_space":"0","_textblocks_10_bottom_space":"field_621977a9f3f4f","textblocks":["paragraph","headline","paragraph","headline","paragraph","headline","paragraph","headline","paragraph","headline","paragraph"],"_textblocks":"field_62197599f3f4a","classname":"","_classname":"field_6204244253a3f","observer_id":"","_observer_id":"field_6204245b53a40","anchor_button_text":"","_anchor_button_text":"field_62b9b0d090a10","anchor_id":"","_anchor_id":"field_6204242753a3e"},"align":"","mode":"edit"} /-->"
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    string(13122) "<!-- wp:acf/ihr-story-introblock {"id":"block_621cfa87b2eab","name":"acf/ihr-story-introblock","data":{"text":"Justin Arenstein is the founder and chief executive of \u003ca href=\u0022https://medium.com/code-for-africa/civic-signal-7592b72b9a38\u0022\u003eCode for Africa\u003c/a\u003e. This is Africa's largest network of civic tech and open data groups. It includes investigative reporters, fact checking groups, and data scientists. He is based in Tbilisi, Georgia.","_text":"field_621cfe1f2ceab","font_color":"black","_font_color":"field_62044418c7d75","background_color":"grey","_background_color":"field_620441bac7d74","dropshadow":"none","_dropshadow":"field_6267f7f921c52","classname":"","_classname":"field_6204244253a3f","observer_id":"","_observer_id":"field_6204245b53a40","anchor_button_text":"","_anchor_button_text":"field_62b9b0d090a10","anchor_id":"","_anchor_id":"field_6204242753a3e"},"align":"","mode":"edit"} /-->

<!-- wp:acf/ihr-textblocks {"id":"block_62197bcf08855","name":"acf/ihr-textblocks","data":{"hide_page_title":"0","_hide_page_title":"field_62d294c9f62e2","textblocks_0_text":"This is an extended cut of the interview from \u003ca href=\u0022https://2022.internethealthreport.org/episodes/the-truth-is-out-there/\u0022\u003eThe Truth is Out There\u003c/a\u003e that has been edited for ease of reading.","_textblocks_0_text":"field_621976bef3f4d","textblocks_0_bottom_space":"0","_textblocks_0_bottom_space":"field_621977a9f3f4f","textblocks_1_text":"Who is actually spreading disinformation?","_textblocks_1_text":"field_621e38d641532","textblocks_1_bottom_space":"0","_textblocks_1_bottom_space":"field_62d29698c68a1","textblocks_2_text":"Whether it be around climate or religion, or reproductive issues and gender rights, you can hire sophisticated agile teams, who build campaigns for you, including by creating fake accounts that reference each other, and appear as coherent communities. They suck people in, and build quite a bit of momentum.\r\n\r\nIn South Africa, we've seen the development of these kinds of networks using xenophobia as a rallying call, where South Africans believe that Nigerians and Kenyans and Zimbabweans and Mozambicans are taking jobs that belong to them. They hit on many of the same trigger points you'll hear in conversations in the US or in Eastern Europe. They use a playbook that has been proven to work elsewhere, but they dress it in local language, and often generate manipulated media to support local claims.\r\n\r\nWe're talking about tens of millions of dollars — and thousands of people employed in Africa and elsewhere — who craft material and amplify it. It's a large industry, and I think the only way we're ever going to bring it under control, is to stop thinking of it in terms of good against evil or of one ideological block versus another. We need to take a step back and map the economy that underlies disinformation. Just like you have a military industrial complex, there is a disinformation industrial complex. And the only way we're going to defang it, is by demonetizing it.","_textblocks_2_text":"field_621976bef3f4d","textblocks_2_bottom_space":"0","_textblocks_2_bottom_space":"field_621977a9f3f4f","textblocks_3_text":"What are some of the techniques you use to expose disinformation?","_textblocks_3_text":"field_621e38d641532","textblocks_3_bottom_space":"0","_textblocks_3_bottom_space":"field_62d29698c68a1","textblocks_4_text":"Trying to fact check everything is whack-a-mole. It's very worthwhile and we need to do it, but it does not scale, because we cannot operate at the same level that these machine generated hate machines do.\r\n\r\nWe are the largest organization in Africa doing disinformation work, and we are only 93 people. We operate across 21 countries, and in those 21 countries there's probably half a billion people. Our fact checking team, which is only 30 people, currently works with 174 newsrooms across Africa to produce maybe 2,000 fact checks a year, which is not a big number. But this has an outsized impact, in that Facebook can label or remove over 6 million posts per year based on those 2,000 fact checks. But even then, it's still a drop in the ocean if you consider the vast quantity of misinformation circulating online.\r\n\r\nThe machine learning tools we use, and the natural language processing tools that we use, don’t just track the use of specific terms, but seek to understand the emerging narratives or conversations — the \u003cem\u003ezeitgeist\u003c/em\u003e that prepares people to be susceptible to the conspiracies that take hold of societies. We would be blind to all of that, if we didn't have machine learning to analyze millions of online articles, and help us to spot trends or outliers.\r\n\r\nWe've built on top of an awesome toolkit called \u003ca href=\u0022https://mediacloud.org/\u0022\u003eMedia Cloud \u003c/a\u003ethat was developed at MIT many years ago, and has been used for tracking media reporting across the world, including with a very big focus on election reporting and coverage. We helped spin up our own version of that, which enables us to track media across different sectors.\r\n\r\nWe've also tapped into increasingly more affordable machine learning tools to map individuals and amplification networks, so we can start to understand how governments and lobbyists are putting together bot armies or troll farms that either amplify the kinds of content or voices that they want, or attack, intimidate, and persecute the kinds of voices that are not in support of their position. We couldn't do that if we didn't have these tools.\r\n\r\nThe algorithms that drive many of these tools are often engineered for Northern audiences or Northern issues, or even just Northern ways of communicating. And by North, I mean Europe and North America. And so, a lot of what we do is try and reverse engineer what those algorithms do, and try and make them more appropriate to the worlds we operate in. You communicate differently on SMS, for example, or on pure text based messaging platforms than you do on a platform where you could also share embedded rich media, like video or audio content. So, understanding some of that communication culture is important for calibrating AI tools.","_textblocks_4_text":"field_621976bef3f4d","textblocks_4_bottom_space":"0","_textblocks_4_bottom_space":"field_621977a9f3f4f","textblocks_5_text":"How else are you using AI?\u003cb\u003e\r\n\u003c/b\u003e","_textblocks_5_text":"field_621e38d641532","textblocks_5_bottom_space":"0","_textblocks_5_bottom_space":"field_62d29698c68a1","textblocks_6_text":"Our team in Cape Town is called \u003ca href=\u0022https://medium.com/code-for-africa/civic-signal-7592b72b9a38\u0022\u003eCivic Signal\u003c/a\u003e and they are a machine intelligence research team. They explore AI technologies to figure out what they're capable of in ‘the real world’ –– by which we mean places like Africa –– where there is not a large amount of data to train your model on. A machine learning brain is only as good as the foundational training that it's had. And if there is not enough information to train it on, you're not going to have very clever machine intelligence. So the team in Cape Town is starting to build some of these large training datasets. They do that by hoovering up certain types of comments, and media reporting from credible media as well as less trustworthy media, so that you can compare. And then we also scoop up content from government communicators. We've built a very large structured data portal. It's the largest on the continent.\r\n\r\nWe're creating the raw material that AI tools need to train themselves on. Unstructured data is super important, but you also need a bit of a knowledge graph or structured data. So, telling an algorithm that this piece of information relates to the following data point in the following way, and placing a value on that relationship. And so what the AI team in Cape Town does is often quite laborious and manual, and not actually done by machines. It’s a bunch of human analysts putting stuff together, so we can feed it into algorithms. We also work with colleagues elsewhere in the world that are pushing human rights focused machine learning, across multiple North American universities. We learn from collaborating with each other, because you really don't want to try to build everything yourself from scratch. So a large part of what the team in Cape Town does is find savvy partnerships and common interests, and then build on those.","_textblocks_6_text":"field_621976bef3f4d","textblocks_6_bottom_space":"0","_textblocks_6_bottom_space":"field_621977a9f3f4f","textblocks_7_text":"What should social media companies be doing to fight this problem at scale?","_textblocks_7_text":"field_621e38d641532","textblocks_7_bottom_space":"0","_textblocks_7_bottom_space":"field_62d29698c68a1","textblocks_8_text":"There are no simple solutions to the challenges they're facing. Language is one big issue. It's very difficult to police content, if you don't understand what is being said. In a country like Nigeria, there are 200+ languages. Ethiopia has similar levels of complexity. In South Africa, there's 11 official languages. If the early warning machine learning algorithms that try to catch a lot of this abuse can't understand, then it's difficult. Human moderation is what the large social media companies fall back on to try to teach the algorithms. But that’s not enough. And it exposes people to horrific content.\r\n\r\nI mean, this is glib, but there should be more collaboration. The platforms should be doing more sharing, and more joint problem solving, to solve a problem that ultimately \u003ci\u003ethey've\u003c/i\u003e created. That means everything from creating standards, to sharing watch lists, to having threat disruption teams from different platforms all sharing notes, so that there's common intelligence to act on.\r\n\r\nSecond, is that they'll never be able to be everywhere, all the time. And so they need to figure out productive and sustainable ways of collaborating with a wider circle of groups: with the media, with fact checkers, with political watchdog ecosystems. All of whom come with slightly different agendas and mandates, which is healthy. Because otherwise there's group-think. I think not enough of that has happened.\r\n\r\nSome platforms are collaborating with global networks of fact checkers, which by its nature is elitist. You've got to adhere to certain standards for transparency and resource limits, to ensure that you can do the work, but that excludes almost everyone. Some fact checkers, for example, get access to algorithmic tools that surface potential misinformation that the public or academic researchers wouldn't have access to. I think we need better transparency into what those accesses are and what the rationale is for how they are controlled. Sometimes, it's to protect the private data of users, but we could push back if we thought those limits were set too conservatively. We could have an informed public debate and discussion. Because we do need to balance privacy with the ability to analyze and root out abusers. We need to figure out ways of creating layered interactions, where grassroots media and people operating at the village level also have access to resources, techniques and AI tools, so their work can cascade up and feed into ever more complex organizations.\r\n\r\nYet another thing is responsiveness. For anyone who's ever tried to report disinformation or abuse on social media, if you hear back from a real human instead of a bot, you're extremely lucky. Platforms need to make better efforts to actually review and respond when people report abuses. We're starting to see some experiments around this. Again, this is a scale issue. Platforms need to understand, they can't do this on their own.\r\n\r\n\u003ci\u003eMozilla has taken reasonable steps to ensure the accuracy of the statements made during the interview, but the words and opinions presented here are ascribed entirely to the interviewee.\u003c/i\u003e\r\n\r\nPhoto of \u003ca href=\u0022https://www.flickr.com/photos/mohamedn/29682340744/\u0022\u003eJustin Arenstein\u003c/a\u003e is by \u003ca id=\u0022yui_3_16_0_1_1662979057820_1398\u0022 class=\u0022owner-name truncate\u0022 title=\u0022Go to Mohamed Nanabhay’s photostream\u0022 href=\u0022https://www.flickr.com/photos/mohamedn/\u0022 data-track=\u0022attributionNameClick\u0022\u003eMohamed Nanabhay\u003c/a\u003e on flickr (CC-BY) 2016","_textblocks_8_text":"field_621976bef3f4d","textblocks_8_bottom_space":"0","_textblocks_8_bottom_space":"field_621977a9f3f4f","textblocks":["paragraph","headline","paragraph","headline","paragraph","headline","paragraph","headline","paragraph"],"_textblocks":"field_62197599f3f4a","classname":"","_classname":"field_6204244253a3f","observer_id":"","_observer_id":"field_6204245b53a40","anchor_button_text":"","_anchor_button_text":"field_62b9b0d090a10","anchor_id":"","_anchor_id":"field_6204242753a3e"},"align":"","mode":"edit"} /-->"
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