1 00:00:00,110 --> 00:00:14,519 *34C3 preroll music* 2 00:00:14,519 --> 00:00:15,969 Herald: The next speaker is born and 3 00:00:15,969 --> 00:00:21,650 raised in Germany. He lives and works as a PhD student in Canada as a member of a 4 00:00:21,650 --> 00:00:27,320 research group on extremist politics in democratic systems and he'll give us an 5 00:00:27,320 --> 00:00:32,640 insight into the public discourse in Germany focused on the so called 6 00:00:32,640 --> 00:00:37,750 "Alternative für Deutschland". Please welcome Alexander Beyer. 7 00:00:37,750 --> 00:00:45,250 *applause* 8 00:00:45,250 --> 00:00:53,120 Beyer: Thank you very much. Thank you people, for showing up in the Saal Borg, 9 00:00:53,120 --> 00:00:58,980 thank you, the internet, for watching, a very big thank you for the organizers, for 10 00:00:58,980 --> 00:01:03,999 giving me the opportunity to, to give this little talk. Yeah, my name is Alexander 11 00:01:03,999 --> 00:01:11,020 Beyer and everywhere I went this winter, I didn't have to wear a winter jacket 12 00:01:11,020 --> 00:01:15,510 because the temperatures were very mild and I will tell you in a minute why that 13 00:01:15,510 --> 00:01:19,600 matters. As I already said, I'm a member of a 14 00:01:19,600 --> 00:01:27,790 research group in Vancouver, where we look at what happens, how extremist parties and 15 00:01:27,790 --> 00:01:33,940 politics fare in democratic systems and we decided to focus this research project 16 00:01:33,940 --> 00:01:40,229 on the fascinating - for researchers fascinating - case of Germany and asking 17 00:01:40,229 --> 00:01:46,640 the questions, if we can point fingers and is it a viable, a valid 18 00:01:46,640 --> 00:01:53,010 judgment to say, the media, the media is to blame for the rise of the AfD? 19 00:01:53,010 --> 00:01:58,790 For anyone who, who decided at the end of 2017 that they would spend most of 2016 in 20 00:01:58,790 --> 00:02:02,590 hibernation - which seemed like a pretty good idea at the time - I will give a 21 00:02:02,590 --> 00:02:09,770 quick rundown what happened: So, we had election in September and the domino 22 00:02:09,770 --> 00:02:16,520 piece that was Germany fell. Domino piece in a sense, that all around in Europe far- 23 00:02:16,520 --> 00:02:22,660 right parties had considerable success in the past, in the recent past, and Germany 24 00:02:22,660 --> 00:02:26,900 was the sort of last stalwart in Central Europe, where a far-right party did 25 00:02:26,900 --> 00:02:35,760 not get at the government, this happened in September and it did not only 26 00:02:35,760 --> 00:02:40,750 get into government, er, into Parliament, it also, the way that it looks like now, it 27 00:02:40,750 --> 00:02:43,760 might become the official leader of the opposition. 28 00:02:43,760 --> 00:02:49,920 So, when these results came in, pundits were really, really quick to call the 29 00:02:49,920 --> 00:02:55,479 shots. The, the dominating sentiment was, that it 30 00:02:55,479 --> 00:03:00,930 was the media's fault: They took the positions of the AfD and gave 31 00:03:00,930 --> 00:03:08,470 disproportionate amounts of coverage to this far-right extremist party. And this 32 00:03:08,470 --> 00:03:16,350 sentiment had a lot of truthiness to it. So, it had a lot of: "Yeah, sure, I can see 33 00:03:16,350 --> 00:03:21,380 why", right, everyone that opened his newspaper or opened a news website, 34 00:03:21,380 --> 00:03:24,760 stories about the AfD seemed, or about anything.. about something that's related 35 00:03:24,760 --> 00:03:31,839 to the AfD, seemed to dominate coverage. This went along with a little bit of a 36 00:03:31,839 --> 00:03:39,760 felt truth, a truth that was perceived by people, about how the campaigning season 37 00:03:39,760 --> 00:03:43,890 was a lot of season and not a lot of campaigning, despite Martin Schultz's best 38 00:03:43,890 --> 00:03:48,160 efforts. A whole lot of sunshine, but not a lot of 39 00:03:48,160 --> 00:03:55,140 conflict and this was something that then was perceived to be very, uh, well, I don't 40 00:03:55,140 --> 00:04:04,150 want to say very skillfully, but somehow filled by the AfD and and the topics that 41 00:04:04,150 --> 00:04:10,520 are of concern to this party. So what are we doing today here? First 42 00:04:10,520 --> 00:04:17,640 off, I'm a political scientist by trade and political scientists like theory. I 43 00:04:17,640 --> 00:04:22,431 know that this is an event, where [I] figure you might not at the forefront of 44 00:04:22,431 --> 00:04:27,499 everyone's minds, but it is for me, because talking and arguing to political 45 00:04:27,499 --> 00:04:32,550 scientists about theory is kind of like mud-wrestling with a pig: you do that for 46 00:04:32,550 --> 00:04:39,270 two or three hours and then you realize, oh, this pig actually enjoys this. So I'll 47 00:04:39,270 --> 00:04:45,960 be sort of, I have one slide on what we, what previous theories would suggest 48 00:04:45,960 --> 00:04:50,689 has, have happened and how it could have happened and then I'll show you what kind 49 00:04:50,689 --> 00:04:56,520 of data we have collected, to systematically answer this question and 50 00:04:56,520 --> 00:05:02,039 talk about public discourse in Germany and then to the meat and potatoes of the talk, 51 00:05:02,039 --> 00:05:09,090 about how the campaign unfolded in the media and I will than, to end I will show 52 00:05:09,090 --> 00:05:15,169 some more data, that is a bit different, that paints a picture on why this election 53 00:05:15,169 --> 00:05:21,800 was a special election and why it was sort of a perfect storm of an election for a 54 00:05:21,800 --> 00:05:29,919 far-right party and why this actually makes us claim that the media could be 55 00:05:29,919 --> 00:05:35,529 said to have behaved pretty reasonable. As a little teaser. 56 00:05:35,529 --> 00:05:44,259 OK. Theory. One slide. Two possible mechanisms of media effects. There's this 57 00:05:44,259 --> 00:05:52,189 normative, very endearing and wonderful idea, that if you read something, that 58 00:05:52,189 --> 00:05:59,849 someone carefully crafts and he or she constructs an argument, that is well 59 00:05:59,849 --> 00:06:05,999 written, well made, you read this, you take it in, you're persuaded by that, 60 00:06:05,999 --> 00:06:13,409 regardless of what this argument is. 60 years of media research suggests, that this 61 00:06:13,409 --> 00:06:17,330 doesn't happen. Pre-existing opinions are extremely 62 00:06:17,330 --> 00:06:21,930 difficult to change in each and every single one of us, even though we're likely 63 00:06:21,930 --> 00:06:28,089 to admit that "No, no sure, I'm a rational thinker, I take standpoints if they're 64 00:06:28,089 --> 00:06:35,770 convincing to me and I internalize them.", but it's not how this works. 65 00:06:35,770 --> 00:06:42,490 The second possible effect, and the one that will be of of concern to us today at 66 00:06:42,490 --> 00:06:50,560 the core of the presentation, is something that's called Priming. So, the media can't 67 00:06:50,560 --> 00:06:55,009 tell people what to think, it can't persuade people independently of the 68 00:06:55,009 --> 00:07:00,639 previous opinions that people have, but it's really, really successful in telling 69 00:07:00,639 --> 00:07:05,860 people, what to think about. It's super good, the media is super good, reading 70 00:07:05,860 --> 00:07:11,500 something is very effective in bringing something to the front of your mind. 71 00:07:11,500 --> 00:07:18,269 And here I.. here I can tell you, why I told you about my my choice of attire in 72 00:07:18,269 --> 00:07:22,059 winter. A vast majority of you probably thought when I said this "'Oh, I didn't 73 00:07:22,059 --> 00:07:27,909 have to wear a winter jacket', wow, what's.. who is this guy?" But maybe a few of you 74 00:07:27,909 --> 00:07:31,510 thought "Yeah, sure it was pretty mild, that's climate change." 75 00:07:31,510 --> 00:07:37,969 So, without naming the issue, I.. there's a chance, that I primed a few of 76 00:07:37,969 --> 00:07:43,169 you, to consider climate change and pull that in your frontal lobe, at the front of 77 00:07:43,169 --> 00:07:49,490 your mind. This is, this is important, this is a.. the 78 00:07:49,490 --> 00:07:57,050 central thing, that we have to consider if we ask, if the media wrote up a party like 79 00:07:57,050 --> 00:08:03,259 the AfD. Also important to consider here is, that 80 00:08:03,259 --> 00:08:10,780 priming is easier - or there's an indirect effect of priming as well - where a topic 81 00:08:10,780 --> 00:08:17,029 that is owned by a specific party, that's the thing that then favors the party 82 00:08:17,029 --> 00:08:20,749 subsequently. So, if the media writes a lot about 83 00:08:20,749 --> 00:08:28,589 refugees, a xenophobic far-right party, that has this problem of refugees at the 84 00:08:28,589 --> 00:08:38,360 core of their agenda, will reap in benefits in our minds, in that it's agenda will be, 85 00:08:38,360 --> 00:08:44,911 will fall on fertile ground. So far the theory, that's all. So, what did 86 00:08:44,911 --> 00:08:51,040 we do based on this theory? We collected data, lots of data, we have.. we understand this 87 00:08:51,040 --> 00:08:56,740 data.. we understand this text, that we collected to be data and we use natural 88 00:08:56,740 --> 00:09:00,670 language processing to analyze that. Natural Language Processing basically 89 00:09:00,670 --> 00:09:04,900 means, that we're giving language to a computer that wasn't written specifically 90 00:09:04,900 --> 00:09:12,100 to be understood by a computer and try to extract meaningful analysis based on what 91 00:09:12,100 --> 00:09:17,829 the computer is doing with this. So, we used some sifting methods to 92 00:09:17,829 --> 00:09:25,770 collect about 8.500 articles from four central German news websites: Focus, Bild, 93 00:09:25,770 --> 00:09:31,860 Welt and Spiegel. And we have.. that results in a unique data set, that, to our 94 00:09:31,860 --> 00:09:39,250 knowledge, no one else has. If so, please reach out to us. 95 00:09:39,250 --> 00:09:45,940 And this was so unique, that it deserves at least six fire emojis. It was also pretty 96 00:09:45,940 --> 00:09:51,350 exciting because, that was pretty cheap. We were two people that were mainly concerned 97 00:09:51,350 --> 00:09:56,639 with collecting this data and I don't want to, I don't want to calculate my hourly 98 00:09:56,639 --> 00:10:03,750 wage, but it was almost done with no financial expense. And this is cool, 99 00:10:03,750 --> 00:10:08,740 because we're social scientists, we're faced with this problem, with this very 100 00:10:08,740 --> 00:10:15,899 interesting case of Germany sort of falling in line, very delayed, with lots of 101 00:10:15,899 --> 00:10:20,430 other countries around it - in terms of the far-right party getting their seats in 102 00:10:20,430 --> 00:10:25,230 Parliament and we can use methods that are available to us, if we're sort of like 103 00:10:25,230 --> 00:10:27,620 sitting down and reading our stack overflow and sort of teaching those 104 00:10:27,620 --> 00:10:32,039 methods to us, to systematically try to answer this question. 105 00:10:32,039 --> 00:10:38,680 Let's dive right in. The share of party mentions in online news. So, what we did 106 00:10:38,680 --> 00:10:45,980 for each day, we calculated what the total number of mentioned political actors is. 107 00:10:45,980 --> 00:10:51,990 We did that based on word lists, that we carefully crafted, that included candidate's 108 00:10:51,990 --> 00:10:56,770 names and party abbreviations and party names and things like "Kanzlerin" and 109 00:10:56,770 --> 00:11:05,670 "Kanzlerkandiat" for the CDU/CSU and the SPD respectively and we let that thing rip 110 00:11:05,670 --> 00:11:12,700 through our little rscript that we have. So, the average of mentions of each party 111 00:11:12,700 --> 00:11:19,269 over the course of the campaign looks something like this: Between July 1st and 112 00:11:19,269 --> 00:11:25,930 September 24th - that's the time frame that we concentrated on - we see a clear 113 00:11:25,930 --> 00:11:31,790 incumbency bonus, the "Kanzlerbonus" the "Kanzlerinbonus" for the CDU/CSU, social 114 00:11:31,790 --> 00:11:40,100 democrats high twenties, and the AfD at 10.7 %. Here we might say at smaller 115 00:11:40,100 --> 00:11:44,450 parties.. a little note to the green and the left, so with this dictionary method is 116 00:11:44,450 --> 00:11:48,990 kind of tricky, because we can't say: "Oh yeah, well we just gonna count every occurrence 117 00:11:48,990 --> 00:11:55,379 of Grüne and Linke" for the Green Party and the Left Party, because then we get stuff 118 00:11:55,379 --> 00:12:00,529 like 'the green banana' and 'the left hand' that is counted for them. So that's why 119 00:12:00,529 --> 00:12:03,960 here we're only using candidates' names. That's why they probably.. they sort of 120 00:12:03,960 --> 00:12:09,329 underperform. But for our purpose of talking about why the AFD got favored by 121 00:12:09,329 --> 00:12:14,360 the media, we're sort of letting that drop under the table. So, the story here is 122 00:12:14,360 --> 00:12:21,740 over the course of the campaign, 10.7 % of mentions were happening that mentioned the 123 00:12:21,740 --> 00:12:28,750 AFD, basically. Case closed. Right, AFD got 12.7 % in the election. That doesn't really 124 00:12:28,750 --> 00:12:36,040 sound like it was favored by the media. And a few of you might know this analysis 125 00:12:36,040 --> 00:12:42,509 from a blog post, that me and Constanze Kurz wrote for Netzpolitik, sort of like 45 126 00:12:42,509 --> 00:12:47,080 seconds after the election, when we worked on truncated data. And we also focused on 127 00:12:47,080 --> 00:12:54,720 print media and this is sort of what this graph looked like, that we based our 128 00:12:54,720 --> 00:12:59,480 conclusion on. AFD didn't really get any disproportionate amount of coverage. It 129 00:12:59,480 --> 00:13:02,480 actually is in the.. in the last week of the campaign.. last weeks of the campaign 130 00:13:02,480 --> 00:13:10,410 actually is outperformed by the FDP. Science is the current state of airing, or 131 00:13:10,410 --> 00:13:18,370 the.. so, now that we have better data in terms of online news data this whole story 132 00:13:18,370 --> 00:13:23,090 looks a bit different. If we take the average over the whole course of the 133 00:13:23,090 --> 00:13:29,139 campaign and actually have it shown to us.. Stay by the day - This is what I want 134 00:13:29,139 --> 00:13:36,029 to focus on now. So just looking at the sort of tail end of 135 00:13:36,029 --> 00:13:41,089 this all the way to the right, when we get close to the election date, the order of 136 00:13:41,089 --> 00:13:46,749 this is surprisingly close to the actual election results. The parties actually do 137 00:13:46,749 --> 00:13:58,379 get in the order, that they came out of the election. But we do see a little curve 138 00:13:58,379 --> 00:14:04,899 that gets closer to a curve that should be bigger. And this is where the.. well, I 139 00:14:04,899 --> 00:14:09,829 don't want to say magic, but this is where the interesting stuff lies. So let's 140 00:14:09,829 --> 00:14:16,639 look at the curves one after the other. The CDU/CSU, as you would expect, as the 141 00:14:16,639 --> 00:14:21,589 incumbent anything that is remotely political in domestic and international 142 00:14:21,589 --> 00:14:26,569 politics, will score mentions for the Chancellor and the CDU/CSU. That's why 143 00:14:26,569 --> 00:14:30,480 this curve is considerably higher than the others, but we do see a downward tendency 144 00:14:30,480 --> 00:14:34,940 the closer we get to the campaign, when campaign coverage shifted from the 145 00:14:34,940 --> 00:14:41,220 incumbent to the competitors. Especially the underdog competitors. Which is kind.. 146 00:14:41,220 --> 00:14:47,670 that's a bad transfer to the SPD now, but if we look at the curve of the 147 00:14:47,670 --> 00:14:52,831 Social Democratic Party, there's a slight bump around August and Martin Schulz 148 00:14:52,831 --> 00:14:59,749 really tried to drive home this issue of justice as the central campaign promise 149 00:14:59,749 --> 00:15:02,870 and there's another little slight humper on September 1st, begin of 150 00:15:02,870 --> 00:15:08,430 September, when the televised debate happened, but the overall trend is pretty 151 00:15:08,430 --> 00:15:13,459 linear, doesn't seem to be, if we would just smooth this plot out to be a straight 152 00:15:13,459 --> 00:15:20,430 line, it probably would be pretty much horizontal. Not so for the AFD. So 153 00:15:20,430 --> 00:15:25,110 remember, over the course of the campaign they got 10.7 % on average of mentions 154 00:15:25,110 --> 00:15:28,930 *drinks from his bottle* And that's true. If we calculate an 155 00:15:28,930 --> 00:15:35,530 average of that, of course, this looks like it scores considerably lower than the two 156 00:15:35,530 --> 00:15:42,100 major parties. But something happens in late August and all of a sudden this party 157 00:15:42,100 --> 00:15:46,699 gets actually close to the Social Democrats. It like.. starting in late 158 00:15:46,699 --> 00:15:52,689 August, the tendency becomes one, that is pretty considerably upwards. And if we 159 00:15:52,689 --> 00:15:57,670 take the average of only the two last weeks before the election, we get to a 160 00:15:57,670 --> 00:16:05,029 number of 19.6 of all mentions are talking about the AFD there. Which is something, if 161 00:16:05,029 --> 00:16:11,019 you think about the mechanisms of priming, those are short term effects. We're 162 00:16:11,019 --> 00:16:14,389 looking for things that happen over a short term or have an effect in a pretty 163 00:16:14,389 --> 00:16:20,570 short term. So this is something that is extremely important. At the beginning of 164 00:16:20,570 --> 00:16:24,650 this time frame, where the plot becomes something that has a trend that shows 165 00:16:24,650 --> 00:16:30,669 upwards, around, like, August 28th - where that first little mountain.. first little 166 00:16:30,669 --> 00:16:35,930 summit occurs - two things happened: one, a refugee boat capsized in the 167 00:16:35,930 --> 00:16:41,170 Mediterranean. An event that we've sadly and.. have to see terrifyingly often and 168 00:16:41,170 --> 00:16:45,199 one of the people died. And the second thing that happened was, that Alexander 169 00:16:45,199 --> 00:16:50,990 Gauland in an interview claimed, that a German politician should be dumped in 170 00:16:50,990 --> 00:17:02,550 Anatolia. And it's interesting, if you talk about.. if you extract the topics, that 171 00:17:02,550 --> 00:17:07,508 are covered in relation to the AFD before and after this moment. 172 00:17:07,508 --> 00:17:15,329 Before this August 28th, it's a lot about Alice Weidel writing emails where it turns 173 00:17:15,329 --> 00:17:20,380 out, she's not the public persona that she claims she is and it's a lot about 174 00:17:20,380 --> 00:17:26,409 internal rifts of this far-right party. The internal tensions between the super 175 00:17:26,409 --> 00:17:35,029 far right wing and the far right or right wing-wing and afterwards, there's a 176 00:17:35,029 --> 00:17:41,610 surprising amount of citations of this "Oh we're gonna... we should dump this person 177 00:17:41,610 --> 00:17:47,179 in another country." So that's something that indicates, that this strategy of sort 178 00:17:47,179 --> 00:17:56,360 of provoking a scandal paid off. But let's.. before we get into that, let's 179 00:17:56,360 --> 00:17:59,740 look into the topics that were covered over the course of the campaign. We did 180 00:17:59,740 --> 00:18:05,270 the same thing, we developed topic dictionaries with keywords for each 181 00:18:05,270 --> 00:18:09,530 category and we let our script read through all the data and count 182 00:18:09,530 --> 00:18:18,520 occurrences. So looking at this, we see a sort of band there in the 10% range, 183 00:18:18,520 --> 00:18:24,640 where it's all a colourful rainbow, where the topics don't really diverge from each 184 00:18:24,640 --> 00:18:29,241 other, except for that topic of domestic security, which 185 00:18:29,241 --> 00:18:34,150 is there at the low end of the range. But we do have one topic, that stands out quite 186 00:18:34,150 --> 00:18:39,700 considerably in the early months of the Wahlkampfsommer, which is European Union, 187 00:18:39,700 --> 00:18:47,010 generally European Union topic. This is because on July 1st Helmut Kohl, the 188 00:18:47,010 --> 00:18:51,950 eternal Chancellor, get the first European act of state and a lot of things were 189 00:18:51,950 --> 00:18:55,909 written about his legacy in terms of the European Union and lots of people showed 190 00:18:55,909 --> 00:19:00,249 up from Strasbourg and Brussels and paid their respects. This is why this topic 191 00:19:00,249 --> 00:19:05,990 seems like.. or this is, why this topic comes in as strong as it does here. Now the 192 00:19:05,990 --> 00:19:10,590 topic that has a sort of unusual curve here on our graph, is the topic of the 193 00:19:10,590 --> 00:19:17,679 environment. Our dictionaries that we developed were topical and so what causes 194 00:19:17,679 --> 00:19:26,090 this steep, steep summit there in early August, is the Dieselgipfel.. there, the 195 00:19:26,090 --> 00:19:31,700 Diesel Summit, where German car manufacturers try to sort of get out of 196 00:19:31,700 --> 00:19:38,669 the fact, that they basically ripped off customers with selling cars that emitted 197 00:19:38,669 --> 00:19:44,250 toxic amounts of poisonous gas and dust. This is why this is extremely important in 198 00:19:44,250 --> 00:19:49,000 the high.. in the low 40 % range in early August. But afterwards the trend 199 00:19:49,000 --> 00:19:55,370 line points steeply down. A topic that was pretty consistent over 200 00:19:55,370 --> 00:20:01,580 the course of the campaign in its overall dynamic or at the sort of.. not the 201 00:20:01,580 --> 00:20:06,460 overall dynamic, but the role that it played, is the topic of immigration. And 202 00:20:06,460 --> 00:20:13,909 immigration means migration and refugees in our case here. And now, thinking about 203 00:20:13,909 --> 00:20:19,980 what that means in relation to our theory on priming, we would think that sure, 204 00:20:19,980 --> 00:20:25,580 that's a topic that is owned by the AFD. It's, like, it's super tightly connected to 205 00:20:25,580 --> 00:20:34,360 that party's rise. So, this is something that does favour a far-right party like 206 00:20:34,360 --> 00:20:40,890 those are, like it is. But we can do a sort of more systematic investigation 207 00:20:40,890 --> 00:20:50,710 into this. So, this graph shows you the poles: each dot represents polling results 208 00:20:50,710 --> 00:20:55,900 for the AFD and the line is the average out of those polls, again over the course 209 00:20:55,900 --> 00:20:59,990 of the of the time frame that we surveyed. Pretty much constant 210 00:20:59,990 --> 00:21:03,840 until mid-August, and all of a sudden we have increasing variance and we have a tendency, 211 00:21:03,840 --> 00:21:11,100 a trend line, that points upwards. And now, this is where the heart of the story lies: 212 00:21:11,100 --> 00:21:19,280 is this, is this dependent on the mentions that the AFD got in the media? 213 00:21:19,280 --> 00:21:23,010 There's the orange line, now we have a sort of we have a different, a different 214 00:21:23,010 --> 00:21:26,470 scale of our graph, that's why it looks way more nervous than in the bigger one, 215 00:21:26,470 --> 00:21:34,669 that we had. Difficult to say. If you have data like this, time serious data, you 216 00:21:34,669 --> 00:21:38,850 actually want to get rid of trends, in terms of what the analysis should be like. 217 00:21:38,850 --> 00:21:44,490 So one way to do this in a graphic representation, is by not showing the 218 00:21:44,490 --> 00:21:49,590 absolute values and how they develop, but only showing the change from day to day 219 00:21:49,590 --> 00:21:57,490 and plotting that. This is what this graph does. So here, these two lines dance around 220 00:21:57,490 --> 00:22:02,390 the zero mark, because - especially the blue one, where it's the polling results - there 221 00:22:02,390 --> 00:22:06,141 wasn't a lot of variation from day to day. It's in incremental steps that the curve 222 00:22:06,141 --> 00:22:13,370 points up and down. It gets a bit more, a bit higher in variance around the.. after 223 00:22:13,370 --> 00:22:20,289 the mid of August. And whereas the AFD mentions in the media, they stay, well, they 224 00:22:20,289 --> 00:22:26,870 stay rich in variance. Hard to tell, if anything systematic is there. You would 225 00:22:26,870 --> 00:22:33,390 think that after the sort of first, 3rd of August, those, those lines are 226 00:22:33,390 --> 00:22:38,890 connected. We ran an analysis - a vector autoregression model, time series 227 00:22:38,890 --> 00:22:45,159 statistics - we couldn't find any systematic relation in a timeframe, that 228 00:22:45,159 --> 00:22:48,790 made sense for our theory on priming. Which is a few days that we're looking 229 00:22:48,790 --> 00:22:53,770 for. So if you talk about time series, we talk about lag and lead, and so you try to 230 00:22:53,770 --> 00:22:58,640 connect a data point that is further down the line with a data point that is, that is 231 00:22:58,640 --> 00:23:07,549 not as far down the line, and nothing of statistical significance showed up here. 232 00:23:07,549 --> 00:23:10,960 And this kind of stumped us - we thought, right, when we looked at this there was 233 00:23:10,960 --> 00:23:18,510 something. That we.. we sort of took a step back and we considered another possibility 234 00:23:18,510 --> 00:23:29,399 to.. as why to.. as to why the media reported as they did. Did the media just give 235 00:23:29,399 --> 00:23:37,659 the people what the people wanted? And here is why I want to talk 236 00:23:37,659 --> 00:23:43,470 to you about why this was a special election. This graph - I adapted 237 00:23:43,470 --> 00:23:48,190 this graph from the Berliner Morgenpost and they based it on on surveys conducted 238 00:23:48,190 --> 00:23:54,380 by Infratest dimap on to the.. the data I didn't have any access to them. But this 239 00:23:54,380 --> 00:24:00,260 impressively shows, why there was a special election. In five out of the six preceding 240 00:24:00,260 --> 00:24:05,350 elections, employment was the topic that was on top of people's minds, when they 241 00:24:05,350 --> 00:24:10,389 made the decision in terms of which party to vote for. And employment means 242 00:24:10,389 --> 00:24:17,749 unemployment. In 2017, with unemployment being at record lows, and after 2015 243 00:24:17,749 --> 00:24:23,720 having.. or having a Syrian civil war still going on, we're having ... 244 00:24:23,720 --> 00:24:32,300 refugees come into.. into Western and into Europe, immigration jumps on out as the.. as 245 00:24:32,300 --> 00:24:38,679 the topic that was the most important for people. And here, if we if we look at also 246 00:24:38,679 --> 00:24:44,760 the topics that are further down the important scale for voters, those are all 247 00:24:44,760 --> 00:24:50,830 topics, where one could conceivably think that those can be spun in a way that they 248 00:24:50,830 --> 00:24:58,040 are connected to this refugee situation. Social injustice, economic injustice, that's 249 00:24:58,040 --> 00:25:02,340 something that a party like the AFD can very effectively turn into an idea on 250 00:25:02,340 --> 00:25:10,560 group based conflict: "It's us versus them". Same with pensions: "Oh, those people come 251 00:25:10,560 --> 00:25:15,320 here to take our jobs and our money, and especially from the old people. From our 252 00:25:15,320 --> 00:25:26,460 elderly. So 2017, die Bundestagswahl 2017, is a special case, if we consider it 253 00:25:26,460 --> 00:25:33,290 compared to other parties. So now having this situation where we find that it's, 254 00:25:33,290 --> 00:25:36,580 it's something that basically never happened in recent history and in Germany 255 00:25:36,580 --> 00:25:42,730 before in terms of what, what made people decide at the polls. We wondered, OK, 256 00:25:42,730 --> 00:25:48,389 well, is there a way to more accurately measure this demand side of things, this 257 00:25:48,389 --> 00:25:55,289 this need for information for.. of voters. And what better way there is to measure 258 00:25:55,289 --> 00:26:03,510 some.. to measure the salience in the population than to look at Google queries? 259 00:26:03,510 --> 00:26:10,260 So we collected Google Trends data - more specifically, the Google searches on 260 00:26:10,260 --> 00:26:13,979 refugees, 'Flüchtlinge', general term, 261 00:26:13,979 --> 00:26:17,780 and again, here's this.. this way to even out a trend 262 00:26:17,780 --> 00:26:25,191 line - this is the daily change in how this topic developed. And if we put 263 00:26:25,191 --> 00:26:31,769 our daily change of AFD mentions over that, we do see that there's something 264 00:26:31,769 --> 00:26:40,370 there. There's some sort of systematic relationship. And then, crunching these 265 00:26:40,370 --> 00:26:44,700 numbers and putting them again through a vector autoregression model, we come to 266 00:26:44,700 --> 00:26:52,970 the conclusion, that with a lag of only one day, Google searches for refugees actually 267 00:26:52,970 --> 00:26:58,559 lead AFD mentions in the media. So, if on Tuesday a higher number of people in 268 00:26:58,559 --> 00:27:06,760 Germany googled "Refugees", on Wednesday, the AFD was mentioned more often than the day 269 00:27:06,760 --> 00:27:12,429 before. The end effect wasn't big, but it was there and it was significant. We also, of 270 00:27:12,429 --> 00:27:17,690 course, considered the alternative, and the magic word is here it's.. it's Granger 271 00:27:17,690 --> 00:27:24,169 causality, so you can actually calculate, and reliably calculate, the temporal 272 00:27:24,169 --> 00:27:34,559 succession, means that one follows the other. And so all of a sudden, it becomes 273 00:27:34,559 --> 00:27:40,809 a bit difficult to point the fingers at the media. Because, if the media just 274 00:27:40,809 --> 00:27:46,460 reacts to an interest, it operates like a business. If we like it or not. There's 275 00:27:46,460 --> 00:27:50,240 the normative idea of the media, especially in a country that is rich in high-quality 276 00:27:50,240 --> 00:27:57,230 publications as is Germany, that the media is a public good, that educates people and 277 00:27:57,230 --> 00:28:01,980 brings out the best in them, in challenging them, and persuading them of 278 00:28:01,980 --> 00:28:05,760 the best side of the argument. But at the end of the day, in your online worlds, it 279 00:28:05,760 --> 00:28:09,250 is a business with a measurable outcome. You have clicks, you have trackers, we 280 00:28:09,250 --> 00:28:14,730 have ad durations that you can measure. And so you can see, which articles are 281 00:28:14,730 --> 00:28:22,770 favored and which articles people last the longest on. And we're not saying 282 00:28:22,770 --> 00:28:26,000 - there's important distinction to make here - we're not saying, that there's a 283 00:28:26,000 --> 00:28:30,730 direct causal link between people googling refugees and the media directly 284 00:28:30,730 --> 00:28:36,650 reacts to that prompt, because there's some search engine optimization guy or 285 00:28:36,650 --> 00:28:43,309 girl.. every media publishing house, that monitors what people are interested 286 00:28:43,309 --> 00:28:48,010 in. We're saying, that there's an intermediate step there. It's not a 287 00:28:48,010 --> 00:28:55,029 direct cause, it's just a sort of delay, that is in there, that allows for other 288 00:28:55,029 --> 00:29:00,850 mechanisms to get in. So we're wondering: What about the consumer focusing 289 00:29:00,850 --> 00:29:08,029 on the demand side? And in 2017, there's a few things that you could actually look at 290 00:29:08,029 --> 00:29:18,269 to gauge what the demand-side demands, and we decided to focus on Twitter. Because, 291 00:29:18,269 --> 00:29:21,029 without actually knowing this, when we first started out with collecting all this 292 00:29:21,029 --> 00:29:27,220 data, we decided to set up, yeah, to set up a Twitter scraper. And that way, between 293 00:29:27,220 --> 00:29:32,640 September 1st and September 24th, we collected 4.5 million tweets, that 294 00:29:32,640 --> 00:29:40,110 contained keywords.. that contain any one of a list of keywords, that had 295 00:29:40,110 --> 00:29:50,179 politic connotation. So looking at this body of data, we can extract things like 296 00:29:50,179 --> 00:29:58,460 the top 200 most used Hashtags. And if we do that and we, we count the tweets that 297 00:29:58,460 --> 00:30:04,540 contains one of the top 200 Hashtags and we pay special attention, to which one of 298 00:30:04,540 --> 00:30:12,759 these Hashtags are decidedly pro AFD, we get to a number, that 30.9 % of the tweets 299 00:30:12,759 --> 00:30:17,549 that contained any of those top 200 Hashtags, actually contain one that is in 300 00:30:17,549 --> 00:30:24,090 favor of the AFD. Whereas if we count the decidedly no AFD, the anti AFD, no AFD in 301 00:30:24,090 --> 00:30:30,759 all ways of spelling and capitalization and so forth, that's only 1.2 %. 302 00:30:30,759 --> 00:30:38,779 And here it becomes a bit ticklish. So, in order to sort of give a 303 00:30:38,779 --> 00:30:46,020 better idea, of what role Twitter might have played in our little, in our little 304 00:30:46,020 --> 00:30:51,049 relationship here, between the demand side and the supply side, the supply side 305 00:30:51,049 --> 00:30:59,281 supplying the news, we have a beautiful network graph. So this is a 306 00:30:59,281 --> 00:31:08,140 retweeting network: this is we extract all the mentions of a.. of an actor. Each dot is 307 00:31:08,140 --> 00:31:13,610 a Twitter user, each line is 5 or more retweets. Retweets, we're aware of that, 308 00:31:13,610 --> 00:31:17,219 Retweets don't automatically mean endorsement - you might retweet something 309 00:31:17,219 --> 00:31:24,549 that is outlandish and crazy. But for the sake of visualizing, what the weights are 310 00:31:24,549 --> 00:31:29,590 on Twitter, we're treating them as the same. And anyone, who ever has worked 311 00:31:29,590 --> 00:31:31,970 with network graphs of that size, that take a long time 312 00:31:31,970 --> 00:31:36,840 to generate, and it's kind of tough to to label them, so I'm very proud, 313 00:31:36,840 --> 00:31:43,750 that I was able to do so. If we look at this Island down there, that blob, that 314 00:31:43,750 --> 00:31:53,640 blue blob - those are accounts, that cluster around AFD accounts. The coloring 315 00:31:53,640 --> 00:31:58,490 here was done by a walk trap algorithm, I just adjusted the colors that 316 00:31:58,490 --> 00:32:03,139 that algorithm used to actually match the colors in the, in the German party 317 00:32:03,139 --> 00:32:11,000 landscape. And so we do have a hefty continent at the bottom right, that 318 00:32:11,000 --> 00:32:16,730 connects all kinds of people to the AFD. There, if you look at the little 319 00:32:16,730 --> 00:32:24,010 appendix below here, that is colored in brown, that is mainly organized around a 320 00:32:24,010 --> 00:32:31,840 movement called Reconquista Europe, which is an even further right wing, right wing 321 00:32:31,840 --> 00:32:40,840 movement that is sort of, like, directly tacked to this island of the AFD, and the 322 00:32:40,840 --> 00:32:47,759 the connecting node is Björn Höcke, which is quite interesting. So we have the AFD down 323 00:32:47,759 --> 00:32:52,659 there, we have the other parties up there, the rainbow, that is the pluralistic political 324 00:32:52,659 --> 00:32:59,749 landscape, we have those.. those two extreme points there, at the at the super top right 325 00:32:59,749 --> 00:33:08,109 and there at the bottom left - that is.. those are very extremely.. extreme Twitter 326 00:33:08,109 --> 00:33:13,590 user parties. That's the ÖDP and the Freien Wähler, so they don't.. they don't seem to 327 00:33:13,590 --> 00:33:19,370 engage with the nodes, that are in the center here. But what's also valiable to 328 00:33:19,370 --> 00:33:25,059 note, is that for the other parties, for the established parties, starting from the left 329 00:33:25,059 --> 00:33:30,480 and orange - the Pirate Party and then red the Social Democrats, Purple is Die Linke, 330 00:33:30,480 --> 00:33:37,159 green die Grünen, yellow FDP and black the Conservative Party CDU/CSU. All of these 331 00:33:37,159 --> 00:33:44,029 parties have a central node - a central, a central account, around which a lot of 332 00:33:44,029 --> 00:33:50,389 other users are fanned out. So there's.. for each party, there's a smaller number, or a 333 00:33:50,389 --> 00:33:55,590 relatively small number, of accounts, that are highly favored in how often they are 334 00:33:55,590 --> 00:34:04,259 retweeted. AFD doesn't have that. Even.. so, this is of course a projection of 335 00:34:04,259 --> 00:34:07,309 something that's 3D in a 2D place, so there might be 336 00:34:07,309 --> 00:34:11,239 some skewing going on here, in terms of how it shows on our screen, but even turning it 337 00:34:11,239 --> 00:34:18,790 and trying to identify which party is at the center, wasn't, wasn't really possible. 338 00:34:18,790 --> 00:34:26,010 So the internal rifts, and the internal power struggles - they do show in how 339 00:34:26,010 --> 00:34:32,360 members of the party are retweeted. Also interesting to note is which nodes, which 340 00:34:32,360 --> 00:34:38,770 users are connecting these two continents, so to speak. One is, that blue dot, is 341 00:34:38,770 --> 00:34:41,860 wahlrecht.de, a polling aggregator, of course, everyone is 342 00:34:41,860 --> 00:34:45,840 interested in getting their polling numbers out. And there's.. that's tough to 343 00:34:45,840 --> 00:34:53,250 see here, but there's a beige user in the middle therem which is welt.de, so 344 00:34:53,250 --> 00:34:58,020 one of the media.. one of the media publications, that we actually collected 345 00:34:58,020 --> 00:35:02,970 data on and surveyed. Another thing that is.. I'm just gonna mention here briefly, is 346 00:35:02,970 --> 00:35:13,830 the.. that light pink colored insert between the greens and the central gray beige dot 347 00:35:13,830 --> 00:35:17,510 - those are Jan Böhmermann, die Heute-show und Extra 3. 348 00:35:17,510 --> 00:35:26,460 *laughing, clapping* Yeah, so there's a.. the dynamics are 349 00:35:26,460 --> 00:35:32,750 clear, that we have this party that is pretty well organized on social media, 350 00:35:32,750 --> 00:35:40,280 and thus is able to dominate a media agenda, that is based on algorithms basically. 351 00:35:40,280 --> 00:35:46,980 If you think about how, how the logic of information dissemination works on 352 00:35:46,980 --> 00:35:54,700 Twitter: with trending Hashtags. If you have a party that is as - well, I don't want 353 00:35:54,700 --> 00:35:58,700 to say organized - but as tightly clustered around itself.. within itself as the AFD 354 00:35:58,700 --> 00:36:05,430 shows up here, there's a good chance, that that will influence, what all of us get to 355 00:36:05,430 --> 00:36:11,800 see, when we check out the Twitter homepage. Now I know, that probably a good chunk of 356 00:36:11,800 --> 00:36:18,080 you have burning questions in their mind, and I'm gonna going to want to know - so 357 00:36:18,080 --> 00:36:25,730 how many of these of these bright blue bar blobs are bots? Are Twitter bots. We tried to 358 00:36:25,730 --> 00:36:31,510 find that out, using a tool called the Botometer, which is something that has an 359 00:36:31,510 --> 00:36:35,740 API available online, where you can submit, it's a project from a research team 360 00:36:35,740 --> 00:36:40,510 in Indiana, where you can submit the name of a Twitter user and then it gives you a.. 361 00:36:40,510 --> 00:36:46,320 it rends lots of lots of analyses and analysis lots of things 362 00:36:46,320 --> 00:36:51,610 about this user: the frequency of tweets, the time at which it tweets, who is.. who 363 00:36:51,610 --> 00:36:56,170 is it following, who is it followed by, who is it talking to, that kind of stuff. But 364 00:36:56,170 --> 00:37:03,640 when I tried to submit that, I broke their API. And so, if they're happen to watch I 365 00:37:03,640 --> 00:37:09,900 apologize, that was me. So it wasn't be able to do so in time, but there's a bunch of 366 00:37:09,900 --> 00:37:13,980 talks tomorrow, that talk about exactly.. about that thing, so I'm happy to have 367 00:37:13,980 --> 00:37:20,210 this sort of as a lead-in for the day tomorrow. So what can we.. go, what can we 368 00:37:20,210 --> 00:37:27,090 take from this? The Bundestagswahl 2017 was a perfect storm for a far-right party like 369 00:37:27,090 --> 00:37:31,570 the AFD. You had a high issue salience of the topic that is at the center of its 370 00:37:31,570 --> 00:37:42,370 agenda, and you have a sort of unregulated Wild West of social media. We'll see 371 00:37:42,370 --> 00:37:46,930 how that changes with recent law changes come into effect, where all of a 372 00:37:46,930 --> 00:37:52,550 sudden the platform itself has some liability, to which kind of messages are 373 00:37:52,550 --> 00:37:57,310 spread. But if that's effective for Twitter, is a whole other bag of 374 00:37:57,310 --> 00:38:03,860 worms. So in that sense, that's why I was.. what I was sorting.. hinting at: in this 375 00:38:03,860 --> 00:38:08,640 issue environment, we have people be interested in the topic that is central 376 00:38:08,640 --> 00:38:16,760 for the party like the AFDs. The media behaved like pretty surprisingly.. 377 00:38:16,760 --> 00:38:22,990 surprisingly predictable? And did not.. at least for the.. for the 378 00:38:22,990 --> 00:38:30,650 topics, or for the publications, that we covered, it did so. And for the context 379 00:38:30,650 --> 00:38:35,660 that we're arguing herein, that the AFD only get like 20 % of the share 380 00:38:35,660 --> 00:38:40,000 towards the end of the campaign, is something that is a little bit surprising. 381 00:38:40,000 --> 00:38:46,820 And that also leads into a different question of what does this "Oh it's the 382 00:38:46,820 --> 00:38:54,750 journalists fault!" actually mean? What does it really mean? This sort of is based 383 00:38:54,750 --> 00:39:00,660 on this normative expectation of the media being an impartial.. an impartial 384 00:39:00,660 --> 00:39:05,920 deliverer of information and if you think about what else is going on on the 385 00:39:05,920 --> 00:39:11,540 internet, with alternative media and an alternative news sphere establishing 386 00:39:11,540 --> 00:39:18,390 itself with news blogs like, well I don't wanna.. I don't want to call any 387 00:39:18,390 --> 00:39:25,730 names, because.. and so, there's a sort of scene of far-right fringe blogs in Germany 388 00:39:25,730 --> 00:39:29,600 that we also collected. And so we're.. further down the line, 389 00:39:29,600 --> 00:39:34,330 we're going to look at what the topics were, that were covered in that and how 390 00:39:34,330 --> 00:39:39,480 that connected to influencing public opinion in Germany, but having said this, 391 00:39:39,480 --> 00:39:44,920 with these alternative ways of getting your news, information being available, if 392 00:39:44,920 --> 00:39:49,930 you have the press, if you have the mainstream press, not covering a party like 393 00:39:49,930 --> 00:39:56,030 the AFD to a certain extent, you only give the fodder to those cries of 394 00:39:56,030 --> 00:40:07,260 "Lügenpresse", mendacious press, in members of the population, that are sort of 395 00:40:07,260 --> 00:40:10,670 at the risk of being lost as audience members. 396 00:40:10,670 --> 00:40:14,950 So, it's kind of difficult to call the.. to call the shots here and actually point 397 00:40:14,950 --> 00:40:18,240 the fingers at the media, because they delivered on informing on an interest that 398 00:40:18,240 --> 00:40:30,050 existed in the in the population, before they reported on something like the AFD. 399 00:40:30,050 --> 00:40:33,520 And with this, I want to leave it at that. I thank you very much for your attention 400 00:40:33,520 --> 00:40:38,500 and I'm highly, highly eager to hear questions and prompts and ideas, how we 401 00:40:38,500 --> 00:40:40,746 could pursue this further. 402 00:40:40,746 --> 00:40:47,726 *applause* 403 00:40:47,726 --> 00:40:55,523 Herald: Vielen Dank, thank you very much. Questions? [unintelligible] any questions? 404 00:40:55,523 --> 00:41:00,480 Feel free to attend the microphones. Even the microphone I don't 405 00:41:00,480 --> 00:41:05,750 see behind the cameras. Let's start with number two. *laughs* 406 00:41:05,750 --> 00:41:08,720 Mic 2: [unintelligible] some sound. Thank you. Thank you very much for your 407 00:41:08,720 --> 00:41:15,760 amazing work. I've got only one question. Do you plan on releasing those 408 00:41:15,760 --> 00:41:21,480 collected data and on what license? Beyer: That's a question that we.. that 409 00:41:21,480 --> 00:41:27,920 we asked ourselves, too. We would love to collect the data and ultimately it will 410 00:41:27,920 --> 00:41:31,040 happen, but we have to make sure, that we actually have the right to do so, with the 411 00:41:31,040 --> 00:41:35,770 way we collected it. But we're definitely looking into that. 412 00:41:35,770 --> 00:41:44,191 Herald: OK, number 5. Yeah, you. Mic 5: OK. Hello. Is this working? Yeah. It's 413 00:41:44,191 --> 00:41:48,170 tempting - I'm from the Netherlands - to compare these experiences with the AFD 414 00:41:48,170 --> 00:41:52,270 with the experience in the Netherlands. You know, we had Wilders, we had Verdonk 415 00:41:52,270 --> 00:41:57,740 we had Fortuyn, now we have Baudet and it seems that there is a major difference 416 00:41:57,740 --> 00:42:02,360 between.. with the AFD, because presently, I have frankly, I don't know the name of 417 00:42:02,360 --> 00:42:07,550 the leader of the AFD, it used to be Frauke Petry and now, I don't know. 418 00:42:07,550 --> 00:42:15,760 But in the Netherlands, the leaders of the.. those populist right-wing parties, they 419 00:42:15,760 --> 00:42:20,990 were.. they were very good in manipulating the media. They were sending out messages 420 00:42:20,990 --> 00:42:27,540 sustaining a "Köder", in Germany, what's the word? Like, um, provocating, sending 421 00:42:27,540 --> 00:42:32,311 out provocations and that attracted attention of the media. So, there are 422 00:42:32,311 --> 00:42:37,070 people saying that you shouldn't react on all provocations, but anyway they were 423 00:42:37,070 --> 00:42:43,570 geared to draw attention and I wonder, whether AFD has been to the same extent 424 00:42:43,570 --> 00:42:52,520 active in the field of drawing attention, purposely using even agencies that are 425 00:42:52,520 --> 00:42:58,230 specialized in advertising. Beyer: Great question. There is this idea, 426 00:42:58,230 --> 00:43:05,100 that the AFD was very skillful at sort of inscenating scandal and purposely doing 427 00:43:05,100 --> 00:43:11,510 things on a public stage that would draw attention to them. For example, this.. 428 00:43:11,510 --> 00:43:17,250 yeah, I say it again.. this expression by Alexander Gauland, to dispose 429 00:43:17,250 --> 00:43:24,030 of a German politician, or the other leading candidate Alice Weidel leaving a 430 00:43:24,030 --> 00:43:32,310 talk show, while it was being broadcast. So there's.. there definitely is this 431 00:43:32,310 --> 00:43:37,351 element of the.. of actually taking a scandal and using it for your own, for 432 00:43:37,351 --> 00:43:44,980 pushing your own agenda, whereas if they used ad agencies for their media campaign, 433 00:43:44,980 --> 00:43:53,270 they did, their campaigning was highly professionalized, in terms of what their 434 00:43:53,270 --> 00:43:57,370 posters were and how their campaign ads were worked. 435 00:43:57,370 --> 00:44:05,400 And they did work with a company, that also was involved with Donald Trump's campaign. 436 00:44:05,400 --> 00:44:12,680 But in terms of.. sort of new media or like online media – it's not that new 437 00:44:12,680 --> 00:44:19,350 anymore – and in terms of what they did on online media, I.. I only have an 438 00:44:19,350 --> 00:44:28,570 anecdotal sense, if they use something like bots, which is also a way of buying, 439 00:44:28,570 --> 00:44:34,760 buying attention. I can.. I can sort of tell you about one specific case, where 440 00:44:34,760 --> 00:44:42,220 we investigated, which Twitter users were the most active in tweeting on the AFD on 441 00:44:42,220 --> 00:44:47,370 German Twitter – tomorrow's a talk about a Twitter user called Ballerina, which is 442 00:44:47,370 --> 00:44:50,460 a name that has been out there which.. there's great education, that that is 443 00:44:50,460 --> 00:44:54,640 definitely a bot, that has been planted and has been controlled by someone else or 444 00:44:54,640 --> 00:45:06,000 by sort of.. by any group of actors that is not actually a ballerina. 445 00:45:06,000 --> 00:45:14,210 What we found was a Twitter user called Teletubbies007, that tweeted in those three 446 00:45:14,210 --> 00:45:22,900 weeks, that we surveyed, 6.500 times and mostly just retweeted, retweeted calls to 447 00:45:22,900 --> 00:45:29,990 go and cast your ballot, that were all put out by the central AFD accounts. And it 448 00:45:29,990 --> 00:45:33,710 didn't have a lot of followers, like something 500 or so, but it just kept 449 00:45:33,710 --> 00:45:39,850 retweeting over and over and over and over. And when we actually wanted to check out 450 00:45:39,850 --> 00:45:46,360 the page of that bot, it was deleted, the user was deleted. 451 00:45:46,360 --> 00:45:51,140 So there's, to answer your question, um, this high.. this degree of 452 00:45:51,140 --> 00:46:00,040 personalization that the Partij voor de Vrijheid has in the Netherlands is not 453 00:46:00,040 --> 00:46:03,180 as extreme for the AFD in Germany, because there's more leading candidates and 454 00:46:03,180 --> 00:46:09,300 there's internal rifts like Geerd Wilders is basically his own party. That's not the 455 00:46:09,300 --> 00:46:15,240 same. But the strategy to use scandal and to use something that is outrageous and 456 00:46:15,240 --> 00:46:18,970 push the boundaries a little bit more, then jump back and say "Oh no, we did not 457 00:46:18,970 --> 00:46:24,260 mean that at all in this way", that is the exact same spot on strategy they used. 458 00:46:24,260 --> 00:46:27,630 Mic 5: Perhaps I should add that Wilders made it like.. 459 00:46:27,630 --> 00:46:31,530 Herald: Excuse me, many people queuing. Mic 5: Okay. Then I'll stop. 460 00:46:31,530 --> 00:46:35,020 Herald: Okay, thank you. We have questions from the internet, then. 461 00:46:35,020 --> 00:46:40,660 Signal Angel: Yes. (?) is asking: "Why did you come to the conclusion that this 462 00:46:40,660 --> 00:46:44,620 was a special election, while the last election in Austria has exactly the same 463 00:46:44,620 --> 00:46:49,350 issues? Don't you see this as some sort of an global effect?" 464 00:46:49,350 --> 00:46:57,350 Beyer: That's true, a Syrian civil war that pushes people to flee from, 465 00:46:57,350 --> 00:47:00,460 from war and save their livelihood, is something that is not only felt in 466 00:47:00,460 --> 00:47:05,050 Germany, but for the context of Germany, it's a special election. That's.. this sort 467 00:47:05,050 --> 00:47:11,330 of situations never.. has never occurred in this way before. But absolutely, each 468 00:47:11,330 --> 00:47:20,740 election in Europe basically since 2015 was a special election in that sense. But 469 00:47:20,740 --> 00:47:25,470 not in terms of the outcomes, in a way, that.. because far-right parties in other 470 00:47:25,470 --> 00:47:30,500 European countries already had, had their foot in the door and especially in Austria, 471 00:47:30,500 --> 00:47:35,550 where.. with the FPÖ were pretty well established with previously having been 472 00:47:35,550 --> 00:47:39,801 part of a government. And now being part of the government again. But 473 00:47:39,801 --> 00:47:45,800 for Germany, in what the issues were, that were top of people's minds: that's the 474 00:47:45,800 --> 00:47:51,370 special case that I meant. Herald: OK, microphone number 3, please. 475 00:47:51,370 --> 00:47:58,120 Mic 3: Thank you, first I really appreciate the sincerity and transparency 476 00:47:58,120 --> 00:48:03,010 of your talk, thank you very much, we need more of this in such circumstances and 477 00:48:03,010 --> 00:48:10,660 maybe less polemics sometimes. There's just a little trifle in your method, where 478 00:48:10,660 --> 00:48:18,190 I was wondering: how did you filter the "Linke" and "Grüne" stuff. Did you.. 479 00:48:18,190 --> 00:48:23,760 yeah, how exactly did you do it? Did you maybe count all the mentions of "Grün" 480 00:48:23,760 --> 00:48:29,840 with a capital and non-capital "G", and "Linke" with a capital L and non-capital 481 00:48:29,840 --> 00:48:34,890 and then filter it out further? Or did you do it the other way around? I know, that 482 00:48:34,890 --> 00:48:41,310 you focused specifically on the AFD stuff, and maybe you were focused on representing 483 00:48:41,310 --> 00:48:46,570 all the parties that might be relevant. But I would still be interested in that 484 00:48:46,570 --> 00:48:50,850 part, thanks. Beyer: That's a great question. The thing 485 00:48:50,850 --> 00:48:55,450 is, that we used.. when we actually put all that.. when after we collected text, before 486 00:48:55,450 --> 00:49:01,510 we put it through the unloading methods, we put it all into lowercase. 487 00:49:01,510 --> 00:49:09,581 Just so we could have a consistent way of analyzing. And with capitalization, it's 488 00:49:09,581 --> 00:49:16,290 kind of.. sometimes it just trips up the way to treat this. And that's why you 489 00:49:16,290 --> 00:49:21,370 ran into these issues with "Linke und Grüne" where we had to resort to only 490 00:49:21,370 --> 00:49:25,510 taking basically the candidates names and then also "Linke Partei" and "Grüne 491 00:49:25,510 --> 00:49:35,750 Partei" and a few conjugations, so "der Linken Partei", "den".. right, like grammatically.. 492 00:49:35,750 --> 00:49:40,500 the cases, we only, like, we conjugated them through. Yeah, but we.. since our focus 493 00:49:40,500 --> 00:49:43,910 was on the AfD, we weren't especially concerned with that, which is 494 00:49:43,910 --> 00:49:48,350 unfortunate, I admit that, but for the purpose of this talk, we decided to just 495 00:49:48,350 --> 00:49:53,500 use this workaround. Mic 3: OK, thanks. 496 00:49:53,500 --> 00:49:56,260 Herald: OK. Microphone six, please. 497 00:49:56,260 --> 00:49:59,130 Mic 6: Hello, thanks for your interesting presentation. 498 00:49:59,130 --> 00:50:03,480 I'm wondering if you and your team.. so, you'd.. you looked at mentions 499 00:50:03,480 --> 00:50:07,760 of the different parties, but I'm wondering if you looked at the content of the articles 500 00:50:07,760 --> 00:50:12,650 and how they talked about it, if they were talked about positively or negatively. 501 00:50:12,650 --> 00:50:15,110 Beyer: Thank you very much, that's a great 502 00:50:15,110 --> 00:50:19,070 question, that we actually did consider. And I'll answer this question with a 503 00:50:19,070 --> 00:50:23,520 counter question, as social scientists like to do. Anyone in this room use Amazon 504 00:50:23,520 --> 00:50:28,591 Mechanical Turk and works on hits to earn a few cents here and there? No? OK, so I 505 00:50:28,591 --> 00:50:37,580 can speak freely. There's a.. there's a method that uses cheap labor on Amazon 506 00:50:37,580 --> 00:50:42,920 Mechanical Turk and presents each worker with two sentences, out of which they have 507 00:50:42,920 --> 00:50:50,000 to change the one that is more positive. And so we wanted to use this to train a 508 00:50:50,000 --> 00:50:55,120 machine learning algorithm to actually get a way to gauge the sentiment of positive 509 00:50:55,120 --> 00:50:58,930 and negative in the text that we had collected. We started that in early 510 00:50:58,930 --> 00:51:06,730 December and we had a, like, a workbook with 4.000 so-called hits, 4.000 little jobs, 511 00:51:06,730 --> 00:51:16,140 4.000 comparisons and when this job was done, five or six days later, we sort of put 512 00:51:16,140 --> 00:51:23,490 that through a test and compared it with our own hand-coding that we had done. 513 00:51:23,490 --> 00:51:31,560 And it turned out that one worker on Amazon Mechanical Turk spent over seven hours and 514 00:51:31,560 --> 00:51:38,510 worked.. of those 4.000 little jobs that we had, he worked 3.980. 515 00:51:38,510 --> 00:51:45,250 And over 1.400 of which he did in less than two seconds. 516 00:51:45,250 --> 00:51:53,840 Which is unfortunate, because: a) this person.. so, this person - right, "Person?, Question 517 00:51:53,840 --> 00:51:59,870 Mark" - probably used a script, probably used a bot or just randomly clicked. The 518 00:51:59,870 --> 00:52:06,770 coding didn't match up at all with what we did hand-wise ourselves and that really 519 00:52:06,770 --> 00:52:13,710 screwed up our approach there. If any of you plan on doing some hits in the new 520 00:52:13,710 --> 00:52:18,860 year for Amazon Mechanical Turk and you're asked to compare two sentences that 521 00:52:18,860 --> 00:52:23,470 mention a political actor in Germany, you can send me an email and maybe a 522 00:52:23,470 --> 00:52:28,110 screenshot and tell me how much you appreciate that we're paying six cents for 523 00:52:28,110 --> 00:52:34,440 each comparison. But that's the story, why we haven't.. we don't have any sentiment in 524 00:52:34,440 --> 00:52:40,260 this analysis here. Herald: [unintelligible] 525 00:52:40,260 --> 00:52:44,840 Mic ?: Hello. I'm from Denmark, so in this context, I'm very much a ghost of 526 00:52:44,840 --> 00:52:47,500 Christmas future. *Beyer laughs* 527 00:52:47,500 --> 00:52:49,040 Mic ?: In your Twitter data, 528 00:52:49,040 --> 00:52:54,660 where you take Retweets as well, do you determine what are quotes and what 529 00:52:54,660 --> 00:53:00,290 are direct Retweets? Because in my experience, and I work with this in Denmark 530 00:53:00,290 --> 00:53:07,360 and in the UK, a lot of people like to distance themselves from what the AfD and 531 00:53:07,360 --> 00:53:15,740 similar are saying by quoting everything they're saying and giving them the press. 532 00:53:15,740 --> 00:53:21,360 Beyer: That's a very good point to make. We did not make any distinction 533 00:53:21,360 --> 00:53:27,020 between quotes and Retweets, but we did filter, based on 5 Retweets, by thinking: 534 00:53:27,020 --> 00:53:31,280 OK, if you occasionally feel like you have to point something out that is 535 00:53:31,280 --> 00:53:36,840 outrageous and ridiculous, that a person, a member of a party, says on Twitter, you 536 00:53:36,840 --> 00:53:41,100 would be inclined to do so less than a certain amount of time. We also tried it 537 00:53:41,100 --> 00:53:45,760 with other cut-offs. The graph basically always looked the same. But if we think 538 00:53:45,760 --> 00:53:52,700 about what this means for how the demand- side is influenced, it doesn't matter. 539 00:53:52,700 --> 00:53:57,210 Basically, if you're retweeting out of endorsement or out of ... out of ... 540 00:53:57,210 --> 00:53:59,420 Mic ?: Spite. Beyer: ... out of spite, that's right. 541 00:53:59,420 --> 00:54:03,630 That's the logic, why we decided to use mentions and Retweets. 542 00:54:03,630 --> 00:54:05,240 Mic ?: Thank you. Herald: Another question 543 00:54:05,240 --> 00:54:09,180 from the internet? Signal: Yes. Luke23 is asking: Do you 544 00:54:09,180 --> 00:54:12,960 think that the window of commonly acceptable ideas, the so-called Overton 545 00:54:12,960 --> 00:54:21,500 window, was shifted to the right by the ideas of the AfD echoed in the media? 546 00:54:21,500 --> 00:54:25,890 Beyer: That's a good question. That's a good question. Something that comes to 547 00:54:25,890 --> 00:54:30,700 mind here, is that media use is epiphenomenal - you're sort of 548 00:54:30,700 --> 00:54:38,130 likely.. but the question is, like: Do you think.. does something happen in you, 549 00:54:38,130 --> 00:54:42,250 because you use a certain media outlet, or do you use a certain media outlet, because 550 00:54:42,250 --> 00:54:48,000 something happened in you ? From the sense that I got, I would say that 551 00:54:48,000 --> 00:54:53,530 the degree to what is.. what is acceptable, definitely was shifted over the course of 552 00:54:53,530 --> 00:54:57,740 this campaign, that all of a sudden we're questioning, if remembering the Holocaust 553 00:54:57,740 --> 00:55:03,770 should be something that is at the heart or very close to German identity. 554 00:55:03,770 --> 00:55:08,810 That's something that a political scientist would have never expected, that 555 00:55:08,810 --> 00:55:15,891 this cleavage can be opened up again in a way that is so potent as it did now. So it 556 00:55:15,891 --> 00:55:22,790 definitely did something to the overall discourse in Germany. Whereas that is an 557 00:55:22,790 --> 00:55:30,580 effect of media reporting on the AFD, would require us to use something like 558 00:55:30,580 --> 00:55:35,930 this.. the sentiment analysis, to actually determine how the media talked about which 559 00:55:35,930 --> 00:55:42,100 aspect of the AFD agendas. 560 00:55:42,100 --> 00:55:47,090 Herald: I can see some movement behind microphone number 8. I'm sorry. *laughs* 561 00:55:47,090 --> 00:55:51,910 Mic 8: Thank you very much. Thank you for your work, I still do have a critical 562 00:55:51,910 --> 00:55:57,930 question. Basically, the things you showed is something like we all know, yeah? We 563 00:55:57,930 --> 00:56:03,030 could see this happening last year, and so - I mean this year, in the last election. So 564 00:56:03,030 --> 00:56:08,710 I am wondering now, whether the method you used, which was basically focusing on 565 00:56:08,710 --> 00:56:15,820 quantity, is in a sort of mirroring what was happening. And I'm wondering if you 566 00:56:15,820 --> 00:56:22,340 would work.. keep working on it. Like, you used buzzwords and you used "the media" 567 00:56:22,340 --> 00:56:31,000 instead of, like, narrowing it down, or more.. using more specific questions and I was 568 00:56:31,000 --> 00:56:37,320 wondering, if you have these results now and you have proof for them? What are your next 569 00:56:37,320 --> 00:56:44,270 questions and how can you continue to use these.. the data you have, to make it more 570 00:56:44,270 --> 00:56:50,500 specific, so we can really have some outcome and some conclusions coming from this? 571 00:56:50,500 --> 00:56:52,870 Beyer: It's a absolutely wonderful question. 572 00:56:52,870 --> 00:56:58,021 Of course, we thought about using this data further down the line. We.. our 573 00:56:58,021 --> 00:57:04,370 initial plan was, to connect this not just with salience data that we derive from 574 00:57:04,370 --> 00:57:07,710 Google searches. We also have Facebook data that we 575 00:57:07,710 --> 00:57:11,980 collected, that we wanted to look into, but there.. it's a bit challenging, to 576 00:57:11,980 --> 00:57:19,780 actually analyze comments in depth onto language, because language tends to be way 577 00:57:19,780 --> 00:57:26,920 more fluid and you have certain problems with selection and self-selection. So you 578 00:57:26,920 --> 00:57:30,480 really, really have to be careful to cross- connect, which person that comments on 579 00:57:30,480 --> 00:57:38,210 Facebook is the same person and thus, if you only do quantitative stuff, would 580 00:57:38,210 --> 00:57:44,210 appear disproportionally. As I mentioned, we have also collected data from far-right 581 00:57:44,210 --> 00:57:52,490 blogs, from "news" blogs, that very actively endorsed the AFD and their topics 582 00:57:52,490 --> 00:57:59,110 and so we're planning to pull this into the analysis along with data from the 583 00:57:59,110 --> 00:58:04,170 German Longitudinal Election Study, where in this time frame, that we surveyed, in the 584 00:58:04,170 --> 00:58:10,800 data, each day 100 people in Germany were called up and asked about their feelings 585 00:58:10,800 --> 00:58:16,250 toward specific parties and actors. So we actually have day-by-day data, once it 586 00:58:16,250 --> 00:58:21,730 comes out, on how people.. what people thought about those actors. So we're 587 00:58:21,730 --> 00:58:28,020 planning to pull that in, as a more reliable measure for salience. 588 00:58:28,020 --> 00:58:30,790 Herald: Thank you very much. I'm very sorry, but time's up, so there will be no 589 00:58:30,790 --> 00:58:35,030 more questions right now in front of the audience. Alexander Beyer, thank you very 590 00:58:35,030 --> 00:58:38,030 much. A warm applause, please. *applause* 591 00:58:38,030 --> 00:58:40,864 Beyer: Thank you. *applause continues* 592 00:58:40,864 --> 00:58:45,950 *postroll music* 593 00:58:45,950 --> 00:59:03,000 subtitles created by c3subtitles.de in the year 2019. Join, and help us!