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Proceedings of the Fourth Italian Conference on Computational Linguistics CLiC-it 2017

 | 
Roberto Basili
, 
Malvina Nissim
, 
Giorgio Satta

Predicting Controversial News Using Facebook Reactions

Angelo Basile, Tommaso Caselli et Malvina Nissim

Résumé

Different events and their reception in different reader communities may give rise to controversy. We propose a distant supervised entropy-based model that uses Facebook reactions as proxies for predicting news controversy. We prove the validity of this approach by running within- and across-source experiments, where different news sources are conceived to approximately correspond to different reader communities. Contextually, we also present and share an automatically generated corpus for controversy prediction in Italian.

Diversi tipi di eventi e la loro percezione in diverse comunità di utenti/lettori possono dare vita a controversie. In questo lavoro proponiamo un modello basato su entropia e sviluppato secondo il paradigma della “distant supervision” per predire controversie sulle notizie usando le reazioni di Facebook come “proxy”. La validità dell’approccio è dimostrata attraverso una serie di esperimenti usando dati provenienti dalla stessa fonte o da fonti diverse. Contestualmente, presentiamo anche un corpus generato automaticamente per la previsione delle controversie in italiano.

Texte intégral

1 Introduction and Background

1The explosion of social media (e.g. Facebook, Twitter, Disqus, Reddit, Wikipedia, among others) and the increased interactions with readers-users that traditional newspapers embraced, have transformed the Web in a huge agora, where news are shared, opinions are exchanged, and debates arise.

2On many topics, such as climate change, abortion, vaccination, among others, people strongly disagree. Following the work by Timmermans et al. (2017), we call controversies situations where, even after lengthy interactions, opinions of the involved participants tend to remain unchanged and become more and more polarized towards extreme values.

3Modeling and understanding controversies may be useful in many situations. Journalists and news agencies may pay additional attention in the framing of a certain news, government officials and policy makers may be more aware of the issues involved in specific laws, social media managers might be more careful, i.e. monitor controversial content, in order to avoid the spreading of hate speech, and the general public may benefit as well thanks to a reduction of the “filter bubble” effect (Pariser, 2011).

4Recently, computational approaches on controversy detection have been developed with varying degrees of success (Awadallah et al., 2012; Borra et al., 2015; Dori-Hacohen and Allan, 2015; Lourentzou et al., 2015). Works in the areas of Sentiment Analysis (Zhou et al., 2013; Deng and Wiebe, 2015; Deng et al., 2013; Chambers et al., 2015; Russo et al., 2015), Emotion Detection (Strapparava and Mihalcea, 2007; Strapparava and Mihalcea, 2008; Russo et al., 2011; Pool and Nissim, 2016), and Stance Detection (Mohammad et al., 2016) are, on the other hand, only partially related, as they focus on predicting/classifying the content of a message with respect to specific categories, such as “positive”, “negative”, “neutral”, or “joy”, “sadness” (among others), or as “being in favour” or “being against”. They may be seen as necessary but not sufficient tools for detecting/predicting controversy (Timmermans et al., 2017).

5The main contribution of this work is two-fold:

6i.) we propose a distant supervised entropy-based model to predict controversial news; and ii.) we present and share an automatically created corpus to train and test models for controversy detection. At this stage of development, we focused only on Italian, although the methods are completely language independent and can be reproduced for any language for which news are available on Facebook. The remainder of the paper is structured as follows: Section 2 illustrates the methods used to collect the data and develop the entropy-based model. Section 3 reports on the experiments and results both in a within- and across-source setting. Finally, Section 4 draws conclusions and outlines future research. Data and code are made available at https://anbasile.github.io/​predictingcontroversy/​.

Table 1: Sample rows from the dataset showing how entropy varies in relation to the reactions.

ID

TEXT

LIKE

LOVE

ANGRY

HAHA

WOW

SAD

entropy

1.)

In volo sul Piemonte con biplano anni ‘30

32

0

0

0

0

0

0.0

2.)

Medico anti vaccini radiato

5700

216

220

36

42

22

0.5

3.)

Piacenza, abbattuto il cinghiale Agostino

125

7

34

33

5

78

1.9

2 Data and Methodology

7We used the Facebook Graph API1 to download news headlines (including the description and body fields) from four major Italian newspapers. Of these, two are slightly politically biased (Corriere della Sera and La Repubblica, both centre/centre-left), two openly biased ones (Il Manifesto, left-wing, and il Giornale, right-wing), and one news agency (ANSA).

  • 2 Since February 2016, Facebook users can react to a post not only with a like but by choosing from a (...)

8Together with each news, we also downloaded all users’ reactions.2 Facebook reactions can be used as a proxy for annotations (Pool and Nissim, 2016), allowing to train a model for predicting the degree of controversy associated to news. On the basis of the definition of controversy previously introduced, our working hypothesis is that if users’ reactions fall in two or more emotion classes (not necessarily opposed in terms of “polarity”) with high frequencies, the controversy of a news item is higher. Building on this, we assume that entropy can be explanatory in modelling news’ controversy: the higher the entropy, the more controversial the news. To better clarify this aspect, consider the data in Table 1. Each sample is the text of a Facebook post, for which we report the reaction breakdown (including LIKE), and its overall entropy based on reaction counts. Users expressing different reactions suggest that a text is likely to be controversial as it is shown by the high values of the entropy, as illustrated in examples 2.) and 3.) vs. example 1.).

  • 3 The full text of the article is not always available or accessible. Furthermore, there is a monthly (...)

9For each source, namely the newspaper pages mentioned at the beginning of this section, we downloaded a collection of posts which appeared between mid-April and early July 2017. Posts with less than 30 reactions in total were discarded. For each post, we collected: i.) the link to the full article on the source’s website (a large majority of the posts include this); ii.) an excerpt of the article (the variable text); iii.) additional texts commenting the article, when available (the variable descriptor); iv.) the full list of users’ reactions. Finally, for a portion of the posts (1024 out of 3595, i.e. 28,48%; column “# body” in Table 2) we downloaded the entire text of the article (the variable body).3 Table 2 provides an overview of the data collected, including, for each source, the number of Facebook posts, the number of tokens, the number of posts for which the full article was retrieved, the token-post ratio, i.e. the number of tokens per post, and, finally, the average entropy.

Table 2: Dataset shape and average entropy score (avg H) per source

SOURCE

# POSTS

# TOKEN

# BODY

RATIO TOKEN-POST

AVG H

AgenziaANSA

883

18,635

528

21.10

1.0216

corrieredellasera

594

23,811

124

40.08

0.9135

ilgiornale

1,022

8,665

124

8.47

1.1266

ilmanifesto

752

36,479

124

48.5

0.6195

repubblica

344

7,763

124

22.56

0.9078

total

3,595

95,353

1,024

26.52

0.9386

  • 4 April 25th is a national holiday in Italy to celebrate the end of World War II.

10To further verify the soundness of using entropy as an indicator of controversy, we inspected the top-10 and bottom-10 news in the full dataset sorted by entropy (high values on top, high controversy) and manually assigned them to a topic. Table 3 illustrates the results for the top 5 and bottom 5 posts, in terms of entropy score. In addition to identifying a different distribution of topics according to degrees of controversy, we also observed that in some cases, the entities and the specific event mentions interact to generate controversy. For instance, in the case of the “25th April” topic4, the controversial news involves a political actor (i.e. ANPI, the National Association of Italian Partisans), and divisions on the celebration of this day, while the non-controversial news reports on museums being open on that day. The entropy score appears to capture this distinction.

Table 3: Sample of entropy-ranked top-5 and bottom-5 posts

TOPIC

TEXT

TOP

Incident

Fuggono dall’aereo in fiamme ma si fermano per scattare un selfie a pochi metri dall’aereo

25th April

#25Aprile #Anpi: ““Festa di tutti gli italiani”“. Roma divisa, due celebrazioni

Gender/LGBTQ

“Genere: Sconosciuto”. E il Canada gli da’ ragione

Immigration

Emergenza #migranti, nave Rio Segura arrivata a Salerno. A bordo 11 donne incinte, 256 minori e 13 neonati #FOTO

Animals

#Piacenza, abbattuto il cinghiale #Agostino. Da giorni nel parco urbano di Galleana, avrebbe caricato il per-sonale

BOTTOM

25th April

#25aprile, ecco i musei statali aperti’

Movies

“La La Land” meritava la statuetta del miglior film, andata poi a “Moonlight”?’

Sport

Il Presidente della Sampdoria Massimo Ferrero e raggiante per la vittoria nel derby di Genova’

Arts

Quando Eugenio Corti morl, il 4 febbraio 2014, Sebastien Lapaque, sul quotidiano parigino Le Figaro, lo defini “uno degli immensi scrittori del nostro tempo”‘

Arts

New York New York ricostruisce i legami artistici dal ‘28 a meta anni ‘60’

3 Experiments

11We use the ANSA dataset to develop our model. The rationale behind this is that, being ANSA a news agency, the texts should be more objective and the controversy should depend on the event itself rather than by its framing in a specific, potentially biased, community. We treat this task as a regression problem, and use mean squared error (MSE) to measure the performance of our system. As baseline, we use a dummy regressor which always predicts the mean entropy of the train dataset: considering that the values range between 0 and 2.9, with a standard deviation of 0.4, a system that always predicts the mean entropy is already performing reasonably well. Furthermore, this is in line with the average entropy values of each dataset, ranging from 0.6195 (Table 2, Il Manifesto) up to 1.1266 (Table 2, Il Giornale).

12Settings We use two main settings. Firstly, the data for training and testing the model originates from the same Facebook page, and we use cross-validation. Secondly, we train and test across pages, so as to investigate the model’s portability across potentially different communities. This second setting can shed light on the issue of perspective bias, as controversy around a specific topic or entity could exist in one domain (or, in this case, in one community as proxied by Facebook pages) and not in another one. In both settings, we run our best model, developed as described below.

13Features For predicting the entropy of the reactions to a given text, we built a system using a sparse feature representation and an SVM regressor, with the scikit-learn LinearSVR implementation (Buitinck et al., 2013). We used a tf-idf vectorizer to represent the text as both word and character n-grams.

14As sentiment might contribute to controversy prediction (Dori-Hacohen and Allan, 2015), we also extended the features with coarse-grained prior polarity information derived from Sentix (Basile and Nissim, 2013), a resource for Italian automatically mapped from the English SentiWordNet (Esuli and Sebastiani, 2006). We represent each token with the absolute values of its polarity (which in Sentix ranges from -1 to +1). This allows us to ignore the specific positive/negative values, and get a more abstract representation on the subjectivity relevance of a token: high values indicate that the text is rich of subjectivity relevant tokens; 0 means that the text is merely objective. For each post, we then compute the average polarity and encoded it into a separate vector. Missing words in the lexicon are simply skipped.

15Model development For development, as mentioned, we only used ANSA. We experimented with different features and different sizes of texts. In particular, we ran experiments using: i.) only the text variable; ii.) a combination of the text and the descriptor variables; and iii.) a combination of the text, the descriptor, and the body variables. Furthermore, these three basic settings have been extended with the polarity values from Sentix. To fine tune the parameters, a grid-search of the model using a 10-fold cross-validation was conducted. Table 4 reports the results of the different models as well as of the base-lines.

Table 4: Results for the cross-validated ANSA dataset.

DATA

BASELINE

MODEL

+ SENTIX

text

0.24

0.154

0.155

text+descriptor

0.24

0.146

0.148

text+descriptor+body

0.24

0.146

0.148

16The best model shows an improvement of 0.094 MSE with respect to the baseline when extending the variable text with descriptor and body. The use of the variable text alone still beats the baseline, but obtains a lower score than the models which include both the descriptor and the body variables. The extensions with the polarity scores from Sentix decrease the model performances (though still outperforming the baselines). We believe that this behaviour is mainly due to noise in the resource itself and calls for better and more context-oriented sentiment lexicons in Italian. Table 5 summarises the features of the best model, which is based on a combination of the three text variables only: text, descriptor, and body (whenever available), represented as word and character n-grams, ignoring the polarity vectors. This model was used on the reminder of the datasets.

 

17Results on the test set Table 6 illustrates cross-validated results for the newspaper datasets. For comparison and completeness, we report also the results of the cross-validation on the full test set, with and without the extension of the data with ANSA.

18With the exception of Il Giornale, our model always beats the baseline, confirming the validity of the designed approach. Extending the newspaper dataset with the data from ANSA, we can observe a reinforcement of the predicting power of the model, with a range between 0.04 to 0.1 points with respect to the corresponding baselines. The positive effect on Il Giornale dataset can be due to an extension of the number of tokens, since Il Giornale is the dataset with the lowest token-post ration (8,47 tokens per post), which clearly affects our model.

Table 5: Best model’s settings and features.

PARAMETER

VALUE

SVRC

10

character ngrams

(2,3)

character binary features

True

character normalization

l2

character sublinear tf

False

word ngrams

(1,3)

word binary features

False

word normalization

l2

word sublinear tf

True

Table 6: Cross-validated results on all datasets.

BASELINE

STD

MODEL

STD

ilgiornale

0.21

0.03

0.22

0.04

ilgiornale+ansa

0.23

0.04

0.19

0.03

ilmanifesto

0.15

0.04

0.11

0.04

ilmanifesto+ansa

0.24

0.04

0.14

0.03

repubblica

0.22

0.07

0.18

0.07

repubblica+ansa

0.24

0.04

0.15

0.04

corrieredellasera

0.24

0.06

0.16

0.06

corrieredellasera+ansa

0.24

0.03

0.14

0.04

full_dataset

0.24

0.02

0.17

0.03

full_dataset-ansa

0.24

0.03

0.17

0.04

19Cross-source results in Table 7 are less clear-cut. In these experiments, it clearly emerges that our model works in the large majority of cases, although with no big gains over the baselines. All datasets fail to beat the baseline when predicting controversy on Il Giornale and, on the contrary, training on Il Giornale only fails to beat the base-line when testing on La Repubblica. This suggests that either there must be a difference in the wording used by Il Giornale with respect to the other datasets, or that the controversy is affected by perspective bias associated to different communities.

20On the other hand, slightly politically oriented newspapers (La Repubblica and Il Corriere della Sera) and the ANSA news agency tend to have a homogeneous behavior, being able to correctly predict controversy in highly politically oriented news (see results for Il Manifesto in Table 7). As a matter of fact, the more the post/token ratio is similar between different sources, the better the model works in predicting controversy. For instance, Il Corriere della Sera and Il Manifesto have a very similar post/token ratio (40,08 and 48,5, respectively) and not surprisingly both cross-source experiments beat the baseline.

Table 7: Cross-source results on all datasets.

TRAIN

TEST

BASELINE

MODEL

ilgiornale

ilmanifesto

0.40

0.36

ilgiornale

AgenziaANSA

0.25

0.24

ilgiornale

repubblica

0.26

0.29

ilgiornale

corrieredellasera

0.28

0.26

ilmanifesto

ilgiornale

0.46

0.46

ilmanifesto

AgenziaANSA

0.40

0.40

ilmanifesto

repubblica

0.30

0.28

ilmanifesto

corrieredellasera

0.32

0.29

AgenziaANSA

ilgiornale

0.22

0.23

AgenziaANSA

ilmanifesto

0.31

0.38

AgenziaANSA

repubblica

0.23

0.21

AgenziaANSA

corrieredellasera

0.25

0.23

repubblica

ilgiornale

0.25

0.28

repubblica

ilmanifesto

0.23

0.23

repubblica

AgenziaANSA

0.25

0.23

repubblica

corrieredellasera

0.23

0.20

corrieredellasera

ilgiornale

0.25

0.25

corrieredellasera

ilmanifesto

0.23

0.20

corrieredellasera

AgenziaANSA

0.25

0.21

corrieredellasera

repubblica

0.21

0.18

4 Conclusions and Future Work

21This paper presents a simple regression model to predict the entropy of a post’s reactions based on the Facebook reaction feature. We take this measure as a proxy to predict the controversy of news, where the higher the entropy (indicated by highly mixed reactions), the bigger the controversy. We run experiments both within and across communities, exemplified by the Facebook pages of specific newspapers. As a by-product, we have also automatically generated a first reference corpus for controversy prediction in Italian.

22The results are promising, given that our model beats the baseline in almost all cases in cross-validation of same source data (see Table 6), and in the large majority of cases when applied cross-sources (see Table 7). At this stage of development, we observed that coarse-grained sentiment values are not useful, although this may depend on the quality of the lexicon employed. Test and training on openly biased datasets (e.g. , and vice-versa) results in the lowest entropy, suggesting perspective bias in the different community.

23The approach we have developed is based on discrete linguistically motivated features. This has an impact in the learned model as it is not able to generalise enough when dealing with low-frequency features and unseen data in the test set. To alleviate this issue, we are planning to model the post representations by using word embeddings.

24We are planning to expand the model to account for perspective bias in different communities. News from different sources may be aggregated per event type, for example via the EventRegistry API5, allowing to explore entropy (and polarisation of reactions) on exactly the same event instance. A first step in this direction would be to detect and match Named Entities to approximately identify similar events. At the reaction-level, the obvious next step is to explore and experiment with clusters of reactions (for instance, positive (LIKE, LOVE, AHAH), negative (ANGRY, SAD), or ambiguous (WOW)), instead of treating them all as single and distinct indicators.

25Another follow-up is to extend this work to other social media data, such as Twitter. Twitter does not allow for nuances in reactions in the same way that Facebook does, as only one kind of “like” is provided. However, the substantial use of hash-tags and emojis might offer alternative proxies to capture a variety of reactions. There is plenty of work on the usefulness of leveraging hashtags as reaction proxies both at a coarse and finer level (Mohammad and Kiritchenko, 2015), but this information, to the best of our knowledge, has not been used to predict likelihood of controversy.

Acknowledgments

26One of the authors wants to thank the Spinoza-NWO Project “Understanding Language by Machines” subtrack 3 for making this work possible.

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Notes

1 https://developers.facebook.com/docs/graph-api

2 Since February 2016, Facebook users can react to a post not only with a like but by choosing from a set of 5 different emotions: ANGRY, LIKE, HAHA, WOW, SAD, LOVE.

3 The full text of the article is not always available or accessible. Furthermore, there is a monthly limit to the data that can be downloaded. We made sure that the final dataset we used contained, for each source, the same number of posts for which the full body could be downloaded. This constraint did not apply to ANSA

4 April 25th is a national holiday in Italy to celebrate the end of World War II.

5 http://eventregistry.org

Auteurs

Faculty of ICT, Univ. of Malta, CLCG, Univ. of Groningen, Groningen, NL – a.basile@student.rug.nl

CLTL, VU Amsterdam, CLCG, Univ. of Groningen, Amsterdam/Groningen, NL – t.caselli@gmail.com

CLCG, Univ. of Groningen, Groningen, NL – m.nissim@rug.nl

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