Rajapinnan uutiskirje syksy 2017 // Rajapinta Newsletter autumn 2017

/English version below/

Rajapinta ry on vuonna 2017 perustettu teknologian, yhteiskunnan ja yhteiskuntatieteellisen tutkimuksen kohtaamispaikka. Yhdistyksen tavoitteena on edistää yhteiskuntatieteellistä teknologian tutkimusta sekä digitaalisten ja laskennallisen menetelmien käyttöä yhteiskuntatieteissä.

Toimintamme on avointa kaikille opiskelijoille, tutkijoille ja tutkimuksen ystäville! Syksyllä 2017 yhdistys järjestää muun muassa säännöllisiä kuukausitapaamisia, Rajapintapäivät-epäkonferenssin sekä palkitsee ansioituneen opinnäytetyön. Lisätietoja alla .

Meetupit

Järjestämme kuukausittain tapaamisia, joissa kuuntelemme puheenvuoroja työn alla olevista projekteista ja tutkimuksista. Tapaamiset ovat kaikille avoimia.

Syksyn meetupit järjestetään 28.9. (Tampere), 27.10. (Helsinki), 24.11. (Helsinki) aina kello 14 ja kestävät noin kaksi tuntia. Tarkempi paikka ja ohjelma julkistetaan yhdistyksen sähköpostilistalla sekä Facebookissa noin viikkoa tai kahta ennen tapaamista.

Oletko kiinnostunut puhumaan meetupissa? Esitelmät ovat tyypillisesti olleet varsin lyhyitä (noin 30 minuuttia), mutta niistä ollaan yleensä keskusteltu varsin pitkään ja hartaasti. Aiheet ovat vaihdelleet algoritmien vallasta verkko-etnografian tutkimusetiikkaan. Jos kaipaat tarkemmin tietoa, ota yhteyttä meetup /at/ rajapinta.co .

Rajapintapäivät 2.-3.11.2017

Rajapintapäivät ovat epäkonferenssi (unconference), jonka agenda muodostuu epäformaalisti tapahtuman osallistujien yhteistössä. Ensimmäiset Rajapintapäivät järjestään pääkaupunkiseudulla 2. – 3.11. Päivien tarkempi aikataulu ja esityskutsu julkaistaan alkusyksystä. Kaikki teknologiaa, yhteiskuntaa ja digitaalisia menetelmiä kehittävät aiheet ovat erinomaisen tervetulleita mukaan!

Opinnäytetyöpalkinto

Rajapinta ry palkitsee erinomaisia pro gradu tai diplomitöitä, joiden aihepiirit ovat Rajapinnan tavoitteiden mukaisia:

yhteiskuntatieteellisesti pohjautuneita töitä teknologiasta tai
teknologiaa hyödyntäviä yhteiskuntatieteellisiä tutkimuksia.

Palkinto voidaan myöntää opinnäytetyön tieteenalasta riippumatta kunhan se on tehty suomalaiseen korkeakouluun ja se on hyväksytty aikavälillä 1.9.2016-31.8.2017.

Hakuaika päättyy 20.9.2017, lisätietoja: https://rajapinta.co/awards/

Liity jäseneksi

Perustietoa yhdistyksestä sekä yhdistyksen jäseneksi voi liittyä verkossa. Jäsenyys on toistaiseksi maksutonta.

Kerromme toiminnastamme täällä blogissa, Facebookissa sekä sähköpostilistalla: lähetä viesti “subscribe internet-research” osoitteeseen majordomo at helsinki.fi .

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Rajapinta ry is an association that focuses in the study of digital society and application of digital methods to social research.

Our activities are open to students, researchers, and anyone else interested on the topic. In fall 2017, the association organizes meetups, the first annual unconference and awards excellent Masters’ theses. More details below.

Meetups

We have already for a year organized meetups where short presentations on recent activities are discussed. The events are – like all our activities – open for anybody interested.

This fall, meetups are held 28.9. (Tampere), 27.10. (Helsinki), 24.11. (Helsinki) at 2 pm and will last for about two hours. We will advertise the topics about one week in advance in Facebook & our email lists.

Would you like to present in our meetup? The presentations are usually short (max 30 min), followed up by an intensive discussion. Contact us at meetup@rajapinta.co for more details.

Annual Unconference 2.-3.11.2017

We organize the first annual unconference in capital city area 2.-3.11. The aim of unconferences is that the agenda is build bottom-up, upon the wishes of the participants. More details will be delivered in our email list during the fall.

Thesis award

We will award two excellent Master’s thesis focused on the interests of the association. The award can be granted to a Master’s thesis evaluated in any Finnish university and finished 1.9.2016-31.8.2017.

The deadline for applications is 20.9.2017, https://rajapinta.co/awards/

Become a member

If you want to join as a member, check out https://rajapinta.co/association/ . This year, the membership is free.

We communicate about our activities in Facebook and on our email list. You can join the list by sending message “subscribe internet-research” to address majordomo at helsinki.fi .

Teknologia, demokratia ja teknologinen kansalaisuus

https://www.flickr.com/photos/nickharris1/8026290210
Photo: Nick Harris

Eilen tulevaa syyslukukautta juhlistettiin valtiotieteellisen tiedekunnan sisäpihalla sidosryhmille suunnatun kesäjuhlan merkeissä. Pidimme yhdessä Mika Pantzarin kanssa tapahtumassa lyhyen dialogipuheenvuoron aiheesta teknologia, demokratia ja kansalaisuus. Ohessa oma pointtini lyhyesti.

Teknologia on osa sosiaalisia, taloudellisia ja poliittisia valtarakenteita.

Suomalaiset yhteiskuntatietelijät ovat yllättävän vähän kiinnostuneita teknologiasta ottaen huomioon kuinka keskeisessä roolissa se arjessamme on. Samaan aikaan julkisuudessa on tällä hetkellä on kovasti vallalla eräänlainen uus-deterministinen puhe teknologiasta: väitteet siitä, kuinka algoritmit määrittelvät kaiken julkisen tilan, kertovat olemmeko sairaita vai emme, ja kuinka tekoäly saavuttaa pian tietoisuuden ja valloittaa maailman. Näissä puheissa teknologia nähdään toimijana, joka tuntuu olevan ihmiskontrollin ulottumattomissa.

Tutkijoina, asiantuntijoina ja kansalaisina meidän pitäisi ymmärtää paremmin niitä taloudellis-poliittisia rakenteita, jotka vaikuttavat teknologian taustalla. Vain siten osaamme paremmin suhteuttaa myös yllä kuvattua puhetapaa todellisuuteen.

Teknologisen kansalaisuuden näkökulmasta on merkittävää, että teknologialla tai tarkemmin teknologiayrityksillä on rakenteellista valtaa, joka määrittelee arkeamme ja julkisuuden rakennetta. tätä valtaa käyttävät ennen kaikkea isot amerikkalaisyritykset – le GAFA, eli Google, Apple, Facebook ja Amazon – joiden toimintalogiikkaa ohjaavat taloudelliset intressit. Tämä huolimatta siitä, että niiden mainospuheissa käytetään jatkuvasti kulttuurillista ja sosiaalista retoriikkaa [1].

Siksi teknologinen kansalaisuus tapahtuu tällä hetkellä kaupallisessa kontekstissa ja on kiinni markkinavetoisessa logiikassa: somekohut, Trumpin twiitit ja yhtä lailla myös perinteinen media ohjautuvat pääasiassa talouden logiikalla.

Markkinavetoinen teknologinen kansalaisuus on somekohuja ja närkästymistä, joita sitten mitataan ja analysoidaan, ja joista tehdään uutisia. On vaarana, että  tälainen pulina jää vain pulinaksi, jossa äänekäs vähemmistö vie näkyvyyden (cf. Marcuse ja repressiivinen toleranssi [2]). Luulemme osallistuvamme ja tekevämme politiikkaa, mutta todellisuudessa luomme vain kohun ja datapisteitä markkinoijalle.

Ehkä demokraattisempi teknologinen kansalaisuus voisi olla teknologian käyttöä suoraan kontaktiin valtaapitävien kanssa, erilaisia osallistavia teknologiaprojekteja ja jalkautumista poliitikkojen ja virkamiesten taholta?

Teknologian tutkimuksessa onkin viime aikoina korostettu niin sanottua vastavuoroisen rakentumisen näkökulmaa (mutual constitution of technology). Sanarimpsu tarkoittaa sitä, että teknologia ja ihmistoimijat vaikuttavat toisiinsa ja teknologian merkitys ja käyttötavat rakentuvat sosiaalisen toiminnan kautta.

Tässä mielessä meillä on kansalaisina ja kuluttajina väistämättä myös valtaa vaikuttaa siihen minkälaiseksi teknologia muodostuu, miten sitä käytetään ja miten se ymmärretään. Tätä valtaa kannattaa käyttää: kehittää uusia tapoja käyttää olemassa olevaa teknologiaa demokratian edistämiseen, pitää yllä tietoisuutta teknologian taustalla olevista voimista, ja tuoda rohkeammin yhteiskuntatieteellistä näkökulmaa myös teknologian kehitykseen.

* *

[1] Van Dijck, J., & Nieborg, D. (2009). Wikinomics and its discontents: a critical analysis of Web 2.0 business manifestos. New Media & Society, 11(5), 855–874. http://doi.org/10.1177/1461444809105356

[2] https://en.wikipedia.org/wiki/A_Critique_of_Pure_Tolerance

AoIR 2017 preconference: Less Hate in Politics!

nettikett.pngIf heading to AoIR 2017, consider also joining our preconference on hate speech recognition and prevention:

Less Hate in Politics! Machine Learning and Interventions as Tools to Mitigate Online Hate Speech in Political Campaigns

  • Oct 18th, 9 am – 12:30 pm
  • Dorpat Convention Centre, Tartu, Estonia

Discriminating, hateful speech online, often targeting specific groups and minorities, has become a pressing problem in the societies. Hateful speech is a form of verbal violence that creates enemities, silences debates, and marginalizes individuals and groups from participation online.

What is challenging is that ‘hate speech’ has come to mean a variety of speech acts and other ill-behaviours online, ranging from penal criminal acts to speech which is uncivil and disturbing, but yet to be tolerated. This definitional difficulty is further abused in claims that any limitations of hate speech endanger people’s right to freedom of expression.

Hate speech has been criminalized in many countries and major Internet companies also engage in efforts to limit it. While many social media platforms allow users to flag content as hate speech for moderation purposes, no official follow-up actions take place. Furthermore, the automatic identification of hate speech is limited by lacking tools.

Pre-conference workshop aim and content:

The aim of the workshop is to facilitate the development of tools and processes how the academic community could run interventions which aim to decrease the toxicity in the online space.

We will provide participants a kick-start with computational tools for hate speech recognition. We will also discuss and reflect the challenges of such interventions and examine the opportunities and problems of deploying such systems. Our own experiences – which we reflect in the workshop – emerge from a project where the social media activity of candidates was monitored during the Finnish municipal election campaigns in April 2017.

The workshop will follow an interactive style using both online and offline tools to facilitate discussion.


Clipart by Eggib.

Lectio Praecursoria: Hybrid Narratives – Organizational Reputation in the Hybrid Media System

lectiokuvaPresented in the public defense of Salla-Maaria Laaksonen on June 16th 2017, at the University of Helsinki. [Lektio suomeksi täällä]

* *

Dear Custos, Mr Opponent, ladies and gentlemen,

Once upon a time the Internet was born.

The Internet that allowed us to find information, send messages, to express ourselves. The Internet that grew up and became an essential part of our everyday. Or so it is told.

Last autumn, I attended a professional workshop. The consultant asked us to write down the name of the biggest influencer of our life. Four out of seven participants wrote down “Internet”. I believe this is a feeling many of us can identify.

Indeed, it is a compelling narrative to tell how technology changed everything. We humans are programmed to tell and hear stories. We imagine narratives even to places where they do not exist. For us, narratives are a way to organize our lifeworld, and a means to explain changes such as the one brought by the rise of modern communication technologies and social media.

Actually, it is a more complex narrative to tell. It would be far too easy to say technology did it all. Instead, as I argue in my thesis, we are in the middle of not technological but a technosocial revolution that affects, among other things, the ways how we, as customers and citizens, interact with organizations.

* *

A central concept in my thesis is the concept of hybrid media system. This concept refers to our current media environment, mediated and maybe even amplified by technology. It is a reality, where the forms and logics of traditional media become merged with the forms of social media.

An illustrative example of the hybrid media space is the newsfeed of Facebook, in which updates written by ones peers are shown side by side with news produced by traditional media – either shared by media themselves or by one’s friends. Another prominent example is the collaborative online encyclopedia Wikipedia, where the content produced by users is to a large extent built by referring to news content or content elsewhere on the web. A third example is Google, the search engine that plays a very central role in our everyday, showing and sorting various media content for our queries and needs.

In my dissertation I explore the hybrid media system as a place of telling stories. From this perspective each blog post, status update or tweet is a small story, a fragment of a story, that the technology invites us to share about our everyday experiences. These small stories have become important building blocks of our daily lives. Certain online technologies act as storytelling tools in a very special way: they organize, curate and modify the stories we tell by combining and remixing them. For example Wikipedia functions exactly in such a manner, as well as does any search function in a service.

As we are using these technologies, every day, narratives are formed, and the narrators of these stories are both us humans and the technology on which we narrate. Next, let me explain how I came to this conclusion.

* *

In this dissertation I investigate, how reputation narratives concerning companies and other organizations are formed in the hybrid media system. That means that I am not that interested in the ways how the organizations themselves do marketing or communication. Instead, I am interested in the ways how human actors and non-human actors such as technology together write stories about the organizations.

This approach is actually quite common to reputation studies. Reputation is a concept that refers to the views the stakeholders of the organization have regarding that organization. What makes reputation special compared to its sister concepts such as brands or company images is that reputation always reflects the full historical performance of the organization. That is, reputations connect to the actual doings and deliverables of the organization. Brands and images can be constructed, but reputations need to be earned.

Thus, reputation narratives are not stories told by the organizations themselves. They are narratives told by customers, partners, reporters, analysts and by laypeople. They are stories, that are often based to the real encounters between the organization and its stakeholders – to the real experiences people have had with the organization or with its products and services.

Of course, such stories have always existed. They have been told on market squares, on coffee tables, and in swimming hall saunas. Maybe a friend has told he had a good experience in a restaurant. A neighbor recommended a good handyman to help with renovations.

Technology, however, changes the ways how stories about organizations are born and how they spread. What happens now is that emotional tweets made by fired employees are embedded in the news about shutting down a factory unit. A customer dissatisfied with a hotel room can go and make a public YouTube video that shows the ugly room, and then ends up in the Facebook feeds of thousands of people, and most likely will be eventually covered by traditional media. A horror story of a dishonest car dealer is anonymously spelled out in Suomi24 and ends up in the Google search of a random user – and this happens even years after the original post has been made.

In this dissertation I study these stories from two perspectives. First, using online discussions, Wikipedia material and interviews of professionals I study how such reputation narratives are formed in the hybrid media system. Second, using an experimental setting I investigate how these stories affect the people who read them, and how they are shown as psychological and physiological reactions in our bodies.

* *

Thus, from the perspective of organizational reputation studies I am building a novel approach to reputation by seeing it from the perspective of communication. Traditionally organizational reputation has been studied either as a form of capital, an intangible asset, or as an interpretative element of the organization. In this dissertation I put forth a suggestion that reputation can be seen as a communicative phenomena, which exists as individual mental frame but also as socially constructed narratives. These narratives can have measurable effects to the people consuming them, and hence, to the mental frames of reputation.

* *

One important factor behind these effects is emotion. The results of my dissertation also show that reputation itself is not only rational but also an affective concept. Traditionally reputation research as well as various reputation measurements have focused on rational aspects of reputation: quality of products, leadership, financial success.

However, the psychophysiological measurements conducted in the sub studies of this dissertation show, that good and bad reputation companies elicit different physiological responses in out test participants while they are reading online news and comments concerning these organizations.

Reputation is thus not only about rational evaluation, but also an emotional assessment, embodied in our physiology. That is why reputation unconsciously affects our decisions when for example making choices between brands. And that is why for organizations both reputation and reputation narratives indeed are a form of intangible capital.

Emotions are also a prominent element of the hybrid media system, and of the reputation narratives themselves. The narratives concerning organizations online are often very emotional. Organizations make us love and hate, they drive us to create fan communities and noisy hate groups. The properties of the technology from emojis to like buttons are also inviting us to express our emotions.

The importance of feelings shows also in the ways how communication professionals interpret and evaluate different media forms of the hybrid media. There is an aura of rationality attached to traditional media and an aura of emotionality attached to social media. In particular, the professionals see social media as an arena overwhelmed with emotion and therefore difficult to grasp.

* *

As the main result of this dissertation I propose that the reputation narratives that are born in the online are very specific forms of narrative by nature: they are hybrid reputation narratives.

Hybrid reputation narratives are polyphonic and emotional narratives born in the interaction between human and non-human actors. They are narratives in which the story elements can be stored in databases, searched, and hyperlinked by various, interacting actors, who through their use of the technical platforms generate the reputation narrative from fragmentary story pieces, one time after another. That is why there are no two similar reputation narratives.

So, narratives in this dissertation, are not conceptualized in a traditional sense, as a coherent story that has a beginning, the middle, and the end. Instead, they are new kinds of stories enabled by technologies, which allow for the participation of many authors and many platforms, collecting a narrative from various story pieces here and there. I argue that in such a technological environment the narrator can also be the user who is searching, selecting and clicking; navigating through different texts and images and creating their own, non-linear storyline.

This is a process in which opinions and facts, as well as rational and emotional content become merged, and in which the storytelling power of the technology interacts and intervenes with the storytelling power of the human actors. In the hybrid media system, the user is bestowed with agency and storytelling capacity, but this agency is both limited and enabled by the technology through which the storytelling takes place.

* *

For years, social scientists have been arguing over the relationship between technology and the society. The most extreme stance is known as technological determinism, that is the assumption that the technology determines the development of the social structure in a given society.

In science and technology studies a reconciling approach has been called the mutual shaping approach, suggesting that society and technology are not mutually exclusive to one another but, instead, they influence and shape each other.

This dissertation suggests that technology changes the ways how stakeholders are telling reputation narratives. In the hybrid media system the users’ storytelling capabilities are both enabled and constrained by the technology, on which the stories are being told. Technology and society studies explain this agency with the term affordance, the possibility of an action given by an object or environment.

This influence, however, is not purely technological. It does not refer only to like buttons and smart phones, but also to the forms and social practices born on a given platform, or in the hybrid media system as a system: practices such as taking pictures of our everyday lives, sharing media content to our friends, updating Wikipedia pages according to the editing rules, or expressing emotions using small yellow faces, are all examples of the media logics of the hybrid media system.

In the end the social action and human choices while using the technology affect what kind of stories are told. The specific ways of using technology, the media logics, are affected by the cultural and social context of the hybrid media system. Therefore, hybrid media cannot be studied only as technology, but they cannot be studied without the technology either.

A concrete example that shows the importance of social action is that social media tools were created for personal communication. They were not created to serve as media where people could express their dissent towards organizations or politicians or to start revolutions. Nonetheless, they have grown to have a role as such tools.

This is why it can be stated that the technology changes the way how business and society relationships unfold in the current media system. That is why technology matters, and why also social scientists should show pay attention to it.

Hate speech detection with machine learning — a guest post from Futurice

This blog post is a cross-posting from Futurice and written by Teemu Kinnunen (edits, comments and suggestions given by project participants Matti and Salla from Rajapinta)

* *

(Foreword by Teemu Turunen, Corporate Hippie of Futurice)

The fast paced and fragmented online discussion is changing the world and not always to the better. Media is struggling with moderation demands and major news sites are closing down commenting on their articles, because they are being used to drive an unrelated political agenda, or just for trolling. Moderation practice cannot rely on humans anymore, because a single person can easily generate copious amounts of content, and moderation needs to be done with care. It’s simply much more time consuming than cut and pasting your hate or ads all across the internet. Anonymity adds to the problem, as it seems to bring out the worst in people.

Early this year the nonprofit Open Knowledge Finland approached [Futurice] with their request to get pro bono data science help in prototyping and testing a machine learning hate speech detection system during our municipal elections here in Finland.

The solution would monitor public communications of the candidates in social media and attempt to flag those that contain hate speech, as it is defined by the European Commission and Ethical Journalism Network.

The Non-Discrimination Ombudsman (government official appointed by our government to oversee such matters) would review the results. There are also university research groups involved. This would be an experiment, not something that would remain in use.

After some discussion and head scratching and staring into the night we [at Futurice] agreed to take the pro bono project.

A tedious and time consuming repetitive task is a good candidate for machine learning, even if the task is very challenging. Moderation by algorithms is already done, just not transparently. An example? Perspective API by Jigsaw (formerly Google Ideas) uses machine learning models to score the perceived impact a comment might have on a conversation. The corporations that run the platforms we broadcast our lives on are not very forthcoming in opening up these AI models. The intelligence agencies of course even less so.

So we feel there’s a need for more open science. This technology will reshape our communication and our world. We all need to better understand its capabilities and limitations.

We understand that automatic online discussion monitoring is a very sensitive topic, but we trust the involved parties – specifically the non-discrimination ombudsman of Finland – to use the technology ethically and in line with the Finnish law.

In this article [Futurice’s] Data Scientist Teemu Kinnunen shares what we have done.

Technology

The hate speech detection problem is very challenging. There are virtually unlimited ways how people can express thoughts including also hate speech. Therefore, it is impossible to write rules by hand or a list of hate words, and thus, we crafted a method using machine learning algorithms.

The main goal in the project was to develop a tool that can process messages in social media and highlight the most likely messages containing hate speech for manual inspection. Therefore, we needed to design a process to find potential hate speech messages and to train the hate speech detector during the experiment period. The process we used in the project is described in Fig. 1.

Figure 1: Process diagram for hate speech detection.

At first, a manually labeled training set was collected by a University researcher. A subset from a dataset consists of public Facebook discussions from Finnish groups, collected for a University research project HYBRA, as well as another dataset containing messages about populist politicians and minorities from the Suomi24 discussion board. The training set was coded by several coders to confirm agreement of the data (kappa > .7). The training set was used to select a feature extraction and machine learning method and to train a model for hate speech detection. Then we deployed a trained model that was trained with manually labeled training samples. Next, we downloaded social media messages from a previous day and predicted their hate speech scores. We sorted the list of messages based on predicted hate speech scores and send messages and their scores to a manual inspection. After the manual inspection, we got new training samples which we used to retrain the hate speech detection model.

Feature extraction

Bag-of-features

There are many methods to extract features from text. We started with standard Natural Language Processing methods such as stemming and Bag-of-Words (BoW). At first, we stemmed words in the messages using Snowball method in the Natural Language Toolkit library (NLTK). Next, we generated a vocabulary for bag-of-words using the messages in manually labelled training samples. Finally, to extract features for each message, we computed a distribution of different words in the message i.e. how many times each word in the vocabulary exists in the message.

Some of the words appear nearly in each message, and therefore, provide less distinctive information. Therefore, we gave different weights for each word based on how often they appear in different messages using the Term Frequency – Inverse Document Frequency weighting (TF-IDF). TF-IDF gives higher importance for the words which are only in few documents (or messages in our case).

Word embeddings

One of the problems in bag-of-features is that it does not have any knowledge about semantics of words. The similarity between two messages is calculated based on how many matching words there are in the messages (and their weights from TF-IDF). Therefore, we tried word embeddings which encodes words that are semantically similar with similar vectors. For example, a distance from an encoding of ‘cat’ to an encoding of ‘dog’ is smaller than a distance from an encoding of ‘cat’ to an encoding of ‘ice-cream ’. There is an excellent tutorial to word embeddings on Tensorflow site for those who wants to learn more.

In practice, we used the fastText library with pre-trained models. With fastText, one can convert words into vector space where semantically similar words tend to appear close by each other. However, we need to have a single vector for each message instead of having varying number of vectors depending on the number of words in a message. Therefore, we used a very simple, yet effective, method: we computed a mean of word encodings.

Machine learning

The task in this project was to detect hate speech, which is a binary classification task. I.e the goal was to classify each sample into a no-hate-speech or a hate-speech class. In addition to the binary classification, we gave a probability score for each message, which we used to sort messages based on how likely they were hate speech.

There are many machine learning algorithms for binary classification task. It is difficult to know which of the methods would perform the best. Therefore, we tested a few of the most popular ones and choose the one that performed the best. We chose to test Naive Bayes, because it has been performing well in spam classification tasks and hate speech detection is similar to that. In addition we chose to test Support Vector Machine (SVM) and Random Forest (RF), because they tend to perform very well in the most challenging tasks.

Experiments and results

There are many methods for feature extraction and machine learning that can be used to detect hate speech. It is not evident which of the methods would work the best. Therefore, we carried out an experiment where we tested different combinations of feature extraction and machine learning methods and evaluated their performance.

To carry out an experiment, we needed to have a set of known sample messages containing hate speech and samples that do not contain hate speech. Aalto researcher Matti Nelimarkka, Juho Pääkkönen, HU researcher Salla-Maaria Laaksonen and Teemu Ropponen (OKFI) labeled manually 1500 samples which were used for training and evaluating models.

1500 known samples is not much for such as challenging problem. Therefore, we used k-Fold cross-validation with 10 splits (k=10). In this case, we can use 90% sample for training and 10% for testing the model. We tested Bag-of-Words (BOW) and FastText (FT) (Word embeddings) feature extraction methods and Gaussian Naive Bayes (GNB), Random Forest (RF) and Support Vector Machines (SVM) machine learning methods. Results of the experiment are shown in Fig. 2.

Figure 2: ROC curves for each feature extraction – machine learning method combination. True Positive Rate (TPR) and False Positive Rate (FRP). The FPR axis describes the ratio of mistake (lower is better) and the TPR axis describe the overall success (higher is better). The challenge is to find a balance between TPR and FPR so that TPR is high but FPR is low.

Based on the results presented in Fig. FIGEXP, we chose to use BOW + SVM to detect hate speech. It clearly outperformed other methods and provided the best TPR which was important for us, because we wanted to sort the messages based on how likely they were hate speech.

Model deployment

Based on the experiment, we chose a feature extraction and machine learning method to train a model for hate speech detection. In practice, we used the score of the binary classifier to sort the messages for manual inspection and annotation.

We ran the detector once a day. At first, we downloaded social media messages from a previous day, then predicted hate speech (scored each message) and stored the result in a CSV file. Next, we converted this CSV file to Excel for manual inspection. After manual inspection, we got new training samples which were used to retrain the hate speech detection model.

During the field experiment, we found out that the model was able to sort the messages based on the likelihood of containing hate speech. However, the model was originally trained with more biased set of samples, and therefore, it gave rather high scores also for messages not containing hate speech. Therefore, manual inspection was required to make the final decision for the most prominent messages. Further measures concerning these messages were done by the Non-Discrimination Ombudsman, who in the end contacted certain parties regarding the findings.

Conclusions

In a few weeks, we built a tool for hate speech detection to assist officials to harvest social media for hate speech. The model was trained with rather few samples for such a difficult problem. Therefore, the performance of the model was not perfect, but it was able to find a few most likely messages containing hate speech among hundreds of messages published each day.

In this work, training -> predicting -> manual inspection -> retraining – iteration loop was necessary, because in the beginning, we had quite limited set of training samples and the style of the hate speech can change rapidly e.g. when something surprising and big happens (A terrorist attack in Sweden happened during the pilot). The speed of the iteration loop defines how efficiently the detector works.

Hybridejä mainenarratiiveja, tunteella ja teknologialla – väitöstilaisuus 16.6.2017

Screen Shot 2017-06-06 at 00.19.27VTM Salla-Maaria Laaksonen eli allekirjoittanut väittelee 16.6.2017 kello 12 Helsingin yliopiston valtiotieteellisessä tiedekunnassa aiheesta “Hybrid narratives – Organizational Reputation in the Hybrid Media System“. Tervetuloa mukaan väitöstilaisuuteen kuulemaan akateemista debattia organisaatiomaineesta ja verkkojulkisuudesta! Alla lyhyt yhteenveto tutkimuksesta.

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Tutkin väitöskirjassani sitä, miten yrityksiä ja muita organisaatioita koskevat mainetarinat muodostuvat hybridissä mediatilassa. Tutkimusongelma on kaksitahoinen: tutkin, miten uusi viestintäympäristö vaikuttaa organisaatiomaineen muodostumiseen, ja toisaalta sitä, minkälaisia kognitiivisia ja emotionaalisia vaikutuksia maineella ja mainetarinoilla on. Hybridi mediatila on viestinnän tutkimuksen tuoreehko käsite (Chadwick 2013), joka pyrkii ymmärtämään nykyistä mediamaisemaa. Hybridiys viittaa eri mediamuotojen sekoittumiseen: sosiaalisen median ja perinteisen median sisällöt ja muodot elävät verkkojulkisuudessa vahvasti sekoittuneena.

Tarkastelen väitöskirjassani hybridia mediatilaa tarinankerronnan paikkana. Tästä näkökulmasta jokainen blogikirjoitus tai twiitti on pieni kertomus, jollaisia teknologia kutsuu meitä kertomaan arjesta ja kokemuksistamme. Monet kertomuksista käsittelevät suorasti tai epäsuorasti yrityksiä ja muita organisaatioita – jolloin ne ovat määritelmällisesti mainetarinoita. Niitä on jaettu arjessa aikaisemminkin, mutta teknologia mahdolistaa uudenlaista tarinankerrontaa: tarinat leviävät lähipiiriä laajemmalle, ne arkistoituvat, ja nistä tulee etsittäviä ja muokattavia.

Osa verkon teknologioista toimiikin tarinankerronnan apuvälineinä hyvin erityisellä tavalla: ne järjestävät, kuratoivat ja muovaavat kertomuksia yhdistämällä erilaisia tarinanpalasia yhteen näkymään. Näin toimii esimerkiksi joukkovoimin ylläpidetty tietosanakirja Wikipedia tai verkon sisältöä penkovat hakukoneet. SIksi hybridissä mediassa maineen tarinankertojina toimivat sekä ihmistoimijat että teknologia yhdessä. Väitöskirjani pohjalta esitänkin, että teknologia muuttaa niitä tapoja, joilla sidosryhmät kertovat tarinoita organisaatioista. Verkkojulkisuuden alustoilla syntyvissä tarinoissa sekoittuvat paitsi eri mediamuodot, myös faktat ja mielipiteet sekä rationaalinen ja emotionaalinen sisältö.

Väitöskirjani korostaakin tunteiden merkitystä maineelle. Niin maineen tutkimuksessa kuin erilaisissa mainemittareissakin on perinteisesti keskitty rationaalisiin ominaisuuksiin: tuotteiden laatuun, johtajuuteen, taloudelliseen menestykseen. Maine näyttäisi kuitenkin olevan yhtä paljon myös emotionaalinen käsite. Organisaatioita käsittelevät kertomukset verkossa ovat hyvin tunnepitoisia: yritysten kanssa ihastutaan ja vihastutaan, niiden ympärille rakentuu faniyhteisöjä ja vihaisia kohuyhteisöjä. Teknologian ominaisuudet emoji-hymiöistä tykkää-nappulaan myös kannustavat ilmaisemaan tunteita.

Eikä tunteissa ole kyse vain ilmaisusta. Väitöskirjan osatutkimuksessa osoitettiin, että hyvä ja huono maine näkyvät eri tavoin koehenkilöiden kehollisissa reaktioissa, kun he lukevat yritystä koskevia verkkouutisia tai verkkokommentteja. Maine on siis myös tulkintakehys: tiedostamaton, kehollinen reaktio, joka ohjaa ihmisen toimintaa esimerkiksi ostoksilla valintatilanteessa.

Mainetutkimuksen näkökulmasta rakennankin työssäni uudenlaista kulmaa mainetutkimukseen. Mainetta on perintisesti tutkittu joko organisaation taloudellisena voimavarana tai tulkinnallisena elementtinä sidosryhmien mielissä. Tässä työssä määrittelen maineen viestinnällisenä ilmiönä, joka on olemassa yksilöiden tulkintakehyksenä sekä sosiaalisesti rakentuneina narratiiveina. Mainenarratiiveilla on kuitenkin myös mitattavia vaikutuksia niitä lukeviin ihmisiin ja heidän tulkintakehyksiinsä. Siksi sekä maine että mainetarinat ovat organisaatioille aineetonta pääomaa.

Väitöskirja koostuu viidestä artikkelista ja yhteenvetoluvusta. Artikkeleissa on käytetty neljää eri aineistoa: viestinnän ammattilaisten haastatteluja, sosiaalisen median verkkokeskusteluaineistoja, Wikipedia-aineistoa sekä psykofysiologisia mittauksia. Näin ollen tutkimus yhdistää metodisesti laadullista, narratiivista analyysia kokeelliseen tutkimukseen.

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TLDR; “Hybridi mainetarina syntyy kun 😩 ja 👾 yhdessä käyttäen apunaan📱💻, muodostavat 📜💌📜 , jotka verkkojulkisuudessa 💾 ja 📢 ja joilla on 📉 vaikutuksia 🏭🏨 🏢:lle.” (ref. Your Research, emojified)

Väitöskirjan elektroninen versio on luettavissa E-thesis -palvelussa.

Väitöskirjaa ovat rahoittaneet Liikesivistysrahasto ja Tekes.

Let’s get organized – Rajapinta ry perustettu

2017-01-16-15-55-52

Tapaamisessamme 16.1.2017 pidimme perustamiskokouksen Rajapinta ry -nimiselle yhdistykselle, jonka tarkoituksena on tukea tieto- ja viestintäteknologian yhteiskuntatieteellistä tutkimusta. Täsmällisemmin, PRH:lla nyt hyväksyttävänä olevista säännöistä lainaten:

Yhdistyksen tarkoituksena on ylläpitää, tukea ja kehittää tieto- ja viestintäteknologian yhteiskunnallista tutkimusta, ylläpitää, tukea ja kehittää tietoteknologiaa soveltavia tutkimusmenetelmiä yhteiskuntatieteelliseen tutkimukseen, sekä seurata ja ottaa kantaa tieto- ja viestintäteknologian yhteiskunnallista vaikutusta tai niiden soveltamista koskeviin kysymyksiin. Yhdistys edistää alan tutkimuksen kotimaista sekä kansainvälistä yhteistyötä.

Miksi tällainen yhdistys tarvitaan? Toisin kuin useissa muissa maissa, Suomessa ei ole vielä muodostettu erillistä akateemista tutkimusyksikköä tieto- ja viestintäteknologian yhteiskunnallisista vaikutuksista kiinnostuneille yhteiskuntatieteilijöille. Teemaan erikoistuminen esimerkiksi maisteriopinnoissa ei ole mahdollista missään yhteiskuntatieteellisessä koulutusohjelmassa tällä hetkellä. Siksi päätimme ketterästi ryhtyä rakentemaan tutkimusyhteisöä itse, monitieteiselle pohjalle eri tutkimusorganisaatioiden yhteistyönä. Rajapinta on tutkimusyhteisö tieto- ja viestintäteknologian ja yhteiskunnan välissä oleville tutkijoille ja opiskelijoille.

Toimintaamme kuuluu muun muassa tieteen popularisointia ja ajankohtaisten ilmiöiden asiantuntevaa käsittelyä tämän Rajapinta.co-blogin kautta, kuukausittaiset tutkijatapaamiset sekä vuosittainen Rajapinta-päivä, jossa esitetään alan suomalaista tutkimusta ja kutsutaan kansainvälisiä vierasluennoitsijoita. Tervetuloa meetup-tapaamisiin tutustumaan toimintaamme!

Yhdistyksen toimintaa tukee seuraavat neljä vuotta Koneen Säätiö vuoden 2016 lopulla myönnetyllä apurahalla. Yhdistyksen puheenjohtajana toimii Matti Nelimarkka.

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Same briefly in English: in out meetup on Monday Jan 16th we founded an association for social science orientated ICT research, under the name Rajapinta ry. The purpose of the association is to support and develop social science orientated research on information and communication technologies in the Finnish academia. Currently there are no institutions in Finnish universities that would specialize on ICT with a social scientific orientation, and for example no master programs allow students to specialize in these questions. Therefore, we decided to start a lean format of collaboration between Finnish researchers and universities to promote and support theoretical, methodological and practical issues related to ICTs and the society. Most of our activities are bilingual in Finnish and in English – you are most welcome to our future meetups!