Bizhan Alipour Pijani
email: bizhan.alipourpijani@loria.fr

Abdessamad Imine
email: abdessamad.imine@loria.fr

Michaël Rusinowitch
email: michael.rusinowitch@loria.fr

You Are What Emojis Say About Your Pictures * Language-independent Gender Inference Attack on Facebook

Keywords: Computer systems organization → Embedded systems, Redundancy, Robotics, • Networks → Network reliability, Social network, privacy, inference attack, gender inference, picture, Emojis

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INTRODUCTION

Attribute inference from social network profiles and behaviors is a powerful mean to breach user privacy for malicious purpose or targeted advertisements. Attribute inference amounts to derive private attributes of a target user (such as gender, age, political view, or sexual orientation) from publicly available data.

Recent works have investigated two types of attribute inference attacks on Facebook: friend-based [START_REF] Zhenqiang | You Are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors[END_REF] and behavior-based [START_REF] Abdelberi | You are what you like! Information leakage through users' Interests[END_REF] inference attacks. Friend-based attacks follow the intuition that you are who you know. They work in two steps: the attacker first collects the friend list of the target user, and then from the target user and his/her friend's available data infers target hidden attributes. Behavior-based attacks follow the intuition that you are how you behave. In this attribute inference attack, the attacker monitors user behavior such as liked pages and joined groups to infer his/her private attributes. Most existing inference techniques proceed by analyzing data directly generated by the target user, or data obtained by crawling the user vicinity network. However, in a real scenario, the amount of available information to an attacker is rather small.

Unlike previous studies, we show how to detect Facebook user's gender through his/her shared images. With the huge amount of available information on Facebook, identifying user's gender from their online activities and shared data is an essential mechanism for targeted advertising or privacy breaking [START_REF] Belinic | Personality profile of social media users how to get maximum from it[END_REF]. Gender is a valuable information source in developing more accurate classifiers for inferring other private attributes such as age [START_REF] Peersman | Predicting age and gender in online social networks[END_REF]. In [START_REF] Farahbakhsh | Analysis of publicly disclosed information in Facebook profiles[END_REF], the authors investigated 479k Facebook users to determine the level of privacy awareness. They showed that about one-half of their collected Facebook users hide their gender. Facebook users prefer to hide their gender for two reasons. First, they want camouflage against sexual harassment and stalking. The Facebook search bar lets users track down pictures of their female friends, but not the male ones [START_REF] Lenton | Facebook Wants You To Search Photos Of Your Female Friends At The Beach, But Not Your Male Mates[END_REF]. Second, they want to reduce discrimination. Gender is the direct beneficial information that helps the private sector to present personalized services. Facebook faced criticism for enabling biased discrimination and misinformation. The American Civil Liberties Union (ACLU) 1 accused Facebook of enabling employers to use targeting technology that excludes a woman from receiving job ads for some positions. Additionally, [START_REF] Brouer | Gender (In)Consistent Communication via Social Media and Hireability: An Exploratory Study[END_REF] studied how different kinds of self-presentation information on Facebook interpreted by employers and the subsequent attraction in hiring decisions.

While many Facebook users hide their sensitive attributes (e.g., gender, age, political view), pictures are still available to the public. A social media sharing analysis conducted by The New York Times revealed that 68% of their respondents share images to give people a better sense of who they are and what they care about [START_REF]The Psychology Behind Why We Share on Social Media[END_REF]. Users in social media share pictures to receive feedback for their activities, especially from friends, and acquaintances, provide a great sense of connectedness. However, they lose privacy control on their posted pictures due to extra information (i.e., meta-data) added by thirdparty during the publication process. Let us review this added meta-data that we consider in our attack. (i) Generated alt-text. Facebook has designed and deployed automatic alt-text, a system to identify faces, objects, and themes from photos by applying computer vision technology. This system is proposed to help blind people to feel more connected and involved in Facebook. The alt text generates a summary of the existing content for the image automatically. The technology can reliably recognize a list of 97 concepts (tags), including people (e.g., people count, smiling, child, baby), objects (e.g., car, building, tree, cloud, food), settings (e.g. inside restaurant, outdoor, nature) and themes (e.g., close-up, selfie, drawing) [START_REF] Wu | Automatic Alt-text: Computer-generated Image Descriptions for Blind Users on a Social Network Service[END_REF].

(ii) Emoji. Users in social media use Emoji to express their feelings directly. Since the 2010s, Emojis emerged into communication where Oxford Dictionaries2 announced , commonly known as FACE WITH TEARS OF JOY, as the word of the year. (iii) Emoticons. An Emoticon3 is a representation of human facial expression using only keyboard characters such as letters, numbers, and punctuation marks. They express emotions differently through facial gestures inside text-based communication.

Motivation

To increase awareness of Facebook users about threats on their privacy, we show that from very limited information, even when the user hides his/her owned comments, we can infer the user gender. Previous gender inference attacks on Facebook have two main limitations. First, users friend-based and behavior-based data are extensively considered in the attack process, degrading prediction accuracy in the case of unavailability. Second, it is limited to text-based knowledge. For example, a person's gender identity is constructed through language by using linguistic features associated with male or female writing style on social media, decreasing the prediction accuracy, when texts are multilingual or unavailable. In this work, we relax these two concrete limitations as follows: 1) We exploit non-user generated data (i) alt-text which is computed by Facebook, and (ii) Emojis/Emoticons added by other Facebook users while commenting on the picture. 2) We rely on Emojis/Emoticons as they are not limited to a specific language.

The advantage of Emojis and Emoticons are twofold: it is a universal language, and it is a non-verbal communication way. On the other hand, alt-text is a widely available description of the picture that saves one from image processing tasks. Female and male posted pictures can receive non-English comments as in Figure 1(a), or Emoji only comments as in Figure 1(b) and analyzing this non-user generated data still is sufficient to launch gender inference attack. Note, we selected these female-owned pictures randomly from Facebook. For the sake of privacy, we blur the pictures and commenter's name. Consider the example in Figure 1(a), where the target user can be inferred as male since the picture received the Spanish word lindo. However, a word like linda may lead the inference process to an incorrect result. A possible remedy is to consider other available meta-data such as alt-text and Emojis into an end-to-end learning system. Figure 1(b) shows an example when the Emojis and generated alt-text are the only available data to the attacker. Emojis/Emoticons are language-independent and make the inference attack possible even when the received comments only consist of Emojis/Emoticons. Our work shows that gender inference attack is possible even when, as in previous examples, essential information from the target user and his/her vicinity network is not available.

Problem Description

Using limited amounts of available data, we propose to investigate gender inference attack by leveraging non-user generated data. Therefore our attack is an indirect attack that targets Facebook users even when they are cautious about their privacy, and hides direct generated data such as friend list, liked pages, groups, writing style (e.g., comments), and profile attributes. Inferring gender from non-user generated data (e.g., other Facebook user Emoji preferences) may be deceiving. As shown in [START_REF]6 Types of Images That Elicit an Emotional Response[END_REF], some categories of pictures (e.g., baby and animal images) are more likely to receive strong emotional responses than other pictures. Below, we present an example that the Emojis alone have biased gender inference towards one gender, but reviewing alt-text has fixed the initial wrong guess. The presence of child in the generated alt-text hints that the posted comments, other Facebook users Emoji preference, are not related to the picture owner gender. To circumvent this misleading information in our inference attack process, we filter pictures by using alt-text. We define the picture filtering rules in Sub-section 3.2.2.

Contributions

We are interested in answering the following questions: Is there a significant difference in the usage of Emoji/Emoticon for commenting female and male-owned pictures? Do female and male receive different Emojis/Emoticons for pictures with the same theme and settings (similar alt-text tags)? Do female and male share picture with similar themes, setting, and objects?

To answer these questions, we consider three different scenarios. In the first scenario, we purely rely on the Emojis/Emoticons regardless of the language of the comments as input data to infer the target user gender. Each Emoji/Emoticon is used without any meaning. In the second scenario, we study only the generated alt-text as input data, in order to discover the female and male preferable pictures sharing style on Facebook. We analyze which tags of alt-text generated more for female and male-owned pictures by Facebook. This analysis enables us to show the preferences of picture sharing style between female and male users. As for the third scenario, we consider the correlation of Emojis/Emoticons and tags of alt-text. In this scenario, we aim to discover the preference Emoji/Emoticon usage of other Facebook users by considering picture owner gender and generated alt-text (tags) of that picture.

Our work is based on the following assumptions: 1) We consider the commenters gender as hidden data which is not accessible.

2) We ignore the comments written by the target user. We assume the target user is careful to hide leaking information from his/her posted comments. 3) We do not know the relation between the target user and his/her commenters. 4) We do not consider user profile name for two reasons. First, although some names used only for a particular gender, it is known that the cultural and geographic origin of names have a large impact on the reliability of gender inference methods [START_REF] Santamaría | Comparison and benchmark of name-to-gender inference services[END_REF]. Second, Facebook users may use the shortened name due to privacy concerns. It is a popular tactic to be identifiable only to friends, but not so easily to a stranger.

To the best of our knowledge, we present the first study of gender inference attack on Facebook using target user friends, friends of friends, or ordinary users Emoji preferences. Other users Emojis/Emoticons preferences, in any social media, as a reaction to the observed pictures, posts, or tweets have not studied before this paper.

In the following, the essence of our contributions and improvements over the previous works: 1) Rather than considering the friend-based and behavioral-based data, which might be costly and unavailable in the real scenario, we provide a new approach for gender inference attack by considering picture meta-data. 2) We use Emojis/Emoticons as universal and powerful language to infer the owner picture gender. This inference has an advantage over any text-based inference attack as it is independent of any language restrictions. 3) We conduct experiments to analyze the Emojis/Emoticons usage for commenting female and male posted pictures on Facebook. These experiments enable us to know the commenter preferences in terms of Emojis/Emoticons that are sufficient to infer gender from pictures. 4) We further analyze the relation between Emoji/Emoticon usage and alt-text for female and male-owned pictures. The recognized relationships then help us to devise attacks with higher inference accuracy. This type of attacks takes advantages of generated picture description instead of relying purely on Emojis/Emoticons.

Outline. The paper is organized as follows: we review related work in Section 2. In section 3, we describe our data set preparation. Section 4 presents the gender differences in receiving Emojis. In Section 5, we explain our selected features. Section 6 presents our experimental results. Section 7 discusses the attack process, and we conclude the paper in Section 8.

RELATED WORK

In this section, we review recent works that are related to our research. For that, we consider two aspects: attribute inference attack and usage preference of Emojis.

Gender Inference Attack on Social Media

Profiling users based on their activities has obtained great attention in the past decade. Especially, user profiling based on gender is important for recommendation systems. Recently, researchers have investigated on popular social media platforms to distinguish male and female based on content sharing [START_REF] De Choudhury | Gender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness[END_REF] and behavior [START_REF] Singh | Inferring gender of a Twitter user using celebrities it follows[END_REF]. Prior works claimed that gender prediction is possible from the writing style of the target user [START_REF] Flekova | Analyzing Biases in Human Perception of User Age and Gender from Text[END_REF], word usage [START_REF] Sap | Developing Age and Gender Predictive Lexica over Social Media[END_REF] and phrase choice [START_REF] Preotiuc-Pietro | Discovering User Attribute Stylistic Differences via Paraphrasing[END_REF]. Gender inference attack by evaluating the target user name performed by [START_REF] Karimi | Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods[END_REF] across major social networks. However, [START_REF] Santamaría | Comparison and benchmark of name-to-gender inference services[END_REF] proved that the performance of this type of attack is biased towards countries of origin. The authors of [START_REF] Cheung | An Analytic System for User Gender Identification through User Shared Images[END_REF] propose user gender identification through user shared images in Fotolog and Flickr, two image-oriented social networks. They perform image processing task on each crawled image (in the offline mode), which is not feasible in an online attack.

To sum up, the above works depend on the availability of usergenerated data, which is costly in a real scenario. In contrast, we perform gender inference attacks by relying only on small information that is not under the direct possession of the user. We do not explore the user network, which has two advantages: (i) makes the attack robust even when the entire personal data and his/her vicinity network is unavailable, and (ii) makes the attack suitable for online mode. Additionally, our attack is not limited to textual language as we use Emoji/Emoticon, a universal language. We showed the benefit of non-user generated data analysis to infer the picture owner gender by relying on the textual part of the comments, regardless of the Emoji/Emoticon usage [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF]. However, this work is complementary work to our previous work.

Emoji Usage Analysis

Several works have analyzed Emoji usage in recent years. Researchers have studied the individual intercept on messages containing Emojis [START_REF] Butterworth | Sender Gender Influences Emoji Interpretation in Text Messages[END_REF]. They have performed experiments on how people use Emojis, an emerging universal language for stating emotions in different countries [START_REF] Lu | Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users[END_REF] and culture [START_REF] Barbieri | How Cosmopolitan Are Emojis?: Exploring Emojis Usage and Meaning over Different Languages with Distributional Semantics[END_REF]. Emoji is a rich resource for sentiment analysis and emotion measurement. For example, [START_REF] Ai | Untangling Emoji Popularity Through Semantic Embeddings[END_REF] performs the first quantitative study to correlate Emoji usage to its semantic. Additionally, [START_REF] An | Factors Influencing Emoji Usage in Smartphone Mediated Communications[END_REF] analyzed messages of Wechat 4 , and IM APP users in China, to learn the diversity of usage preferences of Emoji in frequency, type, and sentiment. The diversity and global usage of Emojis lead researchers to perform analysis of Emoji usage according to gender [START_REF] Chen | Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users[END_REF]. This study collected the data through the Kika Keyboard, and they rely on the usage preference of the user himself. This method may be affected in two way: (i) if the user interacts more with opposite-gender friends, his/her Emoji usage may have affected by them [START_REF] Nguyen | Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment[END_REF], and (ii) if the user is careful in choosing the Emojis. Our work is different in two senses. First, we skip the user Emoji usage and rely on other Facebook users Emotional response to solve the above limitations. Second, we engage the content of the picture as a powerful impact on individuals' emotional responses. Emoji can be interpreted differently according to the platform, which might influence [START_REF] Hannah | Understanding Emoji Ambiguity in Context: The Role of Text in Emoji-Related Miscommunication[END_REF]. Besides, some researchers investigated the power of Emoji in the cross-lingual sentiment classification task [START_REF] Chen | Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification[END_REF] and have performed large scale empirical study on how developers used Emoji on GitHub [START_REF] Lu | A First Look at Emoji Usage on GitHub: An Empirical Study[END_REF].

To conclude, all these approaches depend on the target user Emoji's usage. It might be straightforward to guess the Emoji publisher's gender. In contrast, we study gender inference attacks on Facebook by considering Emojis/Emoticon's preferences of other Facebook users (e.g., friends) while commenting on target-owned pictures. Although this approach is more complicated, it has two advantages over previous works: (i) target user personality does not affect the performance, (ii) the attack is still possible even when the target user is careful enough to manipulate Emoji/Emoticon neutrally.

COLLECTED DATA

In this section, we illustrate in detail the data set and the preprocessing steps.

Data Set

We launch our gender inference attack by collecting picture metadata. To that end, we extract each picture meta-data, alt-text, and Emoji(s)/Emoticon(s) of the comments, from the related HTML file. We extract the user gender, when available, to create labeled data sets to be exploited by our supervised machine learning algorithms. We use two labels female and male, corresponding to biological sex. Let U = {u 1 , u 2 , u 3 , .., u m } be the set of target user pictures. For every picture we collect u i =< a i , e i > where a i is an alttext generated by Facebook and e i is the set of Emojis/Emoticons posted by other Facebook users for that picture. We denote (i) A = 0<i ≤m a i the set of all extracted alt-texts, and (ii) E = 0<i ≤m e i 4 https://www.wechat.com/en the set of all extracted Emojis/Emoticons. Later, we create O = 0<i ≤m a i e i which is the co-occurrence of a i and e i of picture i.

Data Pre-processing

We perform three pre-processing steps to clean the collected data as follows:

3.2.1 Cleaning alt-text. Facebook uses 97 different tags to describe the content of each uploaded picture. We reformulate the generated alt-text in three steps, which contain: Conjunction: Facebook uses two conjunctions inside their generated alt-text, which are and, and or. For example, Facebook produces tag like one or more people, if their algorithm is uncertain about the number of people inside the picture. We reformulate this tag to more people.

Redundant Tag: In some pictures, the alt-text comprises repeated tags. Consider the following example where the generated alt-text contains: 3 people, people smiling, people standing, and hat. The people in the first tag refers to the number of people inside the picture, which is 3. However, people in the second and third tags {people smiling, people standing} respectively, is the redundant word. As a result, we reformulate the alt-text to 3 people, smiling, standing, hat.

Text: Facebook in some condition appends the text messages to the generated alt-text if the image has text in it. For example, 4 people, meme, text that says 'WHO ARE YOU IN THE DIFFERENT WORLDS?. We reconstruct the alt-text to 4 people, meme.

These steps aim to clean the alt-text in order to create proper n-grams and co-occurrence, respectively, in the second and third scenarios. These scenarios are detailed in Section 1.3. Note, we do not reorder the alt-text proposed and generated by Facebook.

Picture

Filtering. Facebook generates a description for each picture by using four categories {people, objects, setting, and theme} (detailed in Section 1). To address the problem of selecting useful picture meta-data, we define two rules on the generated alt-text to filter pictures. These rules mean to preserve pictures meta-data, alt-text, and Emoji/Emoticon, in the inference process.

General Rule: We keep the picture meta-data if there is no animals in objects tag, and no child inside the alt-text. In this rule we do not consider the number of recognized people, in people tag, because we are interested in the Facebook users emotional response while observing female and male-owned pictures with similar tags (e.g., 2 people). As a consequence of this rule, the number of people inside the picture can mislead the attack process, detailed [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF]. To solve this problem, we set the second rule on the people tag.

Restricted Rule: We keep the picture meta-data if the generated alt-text contains 1 person. This rule is satisfied when there is only 1 person in people tag, no animals in objects tag, and no child in the generated alt-text.

We apply our picture filtering rules only on the third data set (O). Our analysis is regardless of language, and we do not analyze the words which give a clue about the gender disparity inside the posted comments, like she and he. As a result, we filter the pictures by the alt-text without analyzing the received Emojis.

Emoticons

An Emoticon is a digital icon that conveys a human expression. It usually contains punctuation marks, numbers, and letters. According to Wikipedia5 330 Emoticons, most used Emoticons in different categories, have their corresponding Emoji(s). For example, ; -), and ; ) Emoticons have the corresponding Emoji . To reduce the complexity of the model and make the inference attack effective, we find the best match for each Emoticon and replace it by Emoji. We observe 120 different Emoticons in our data set. To perform this task, we used online services such as The Smiley Dictionary6 , urbandictionary7 , Emojicodes8 , Emoticonr9 , and IM Emoticons10 . For the rest of the paper, we use Emoji to show Emoji/Emoticon.

GENDER BIAS IN RECEIVED EMOJIS

In this section, we discuss the picture owner gender impact on Facebook users emotional responses. We hypothesized that the content of the image along with picture owner gender shape Facebook users response, which evoke a specific emotion in commenting for pictures.

Emoji Popularity

We investigate other Facebook users Emoji(s) preferences while observing female and male-owned pictures with similar alt-text (tags). Developing a comprehensive analysis on Facebook users emotional response while commenting for female and male-owned images drive to gender inference attack. To understand the differences, we collect the top 4 tags of alt-text generated by Facebook for female and male published pictures. Later, we study top 10 Facebook users Emoji preferences in commenting on photos containing these 4 tags (by considering the owner gender). Table 1 indicates that Facebook users used more smiley Emojis (e.g., ) for male images, while emotional Emojis (e.g., ) are commented more for female pictures. The order of these 10 Emojis preferences are from left to right. To avoid the difference in male and female posted pictures number affecting the result, we apply sub-sampling over the female pictures, the dominant one, and calculate the result over the random sub-samples. We discover Facebook users used 1291 different types of Emoji in commenting to female pictures, while they used 877 for male pictures. To conclude, we identify that gender and content of the image affect Facebook users Emoji usage. They have a higher tendency of posting emotional Emoji for female than male pictures.

Emoji Categories and Reaction Modes

4.2.1 Emoji Categories. We use Emoji categories 11 to demonstrate the influence of picture owner gender on Facebook users responses. To that end, we show the Emoji preferences of other Facebook users according to 10 given categories. We discover Facebook users frequently use Emojis from Smileys and Symbols categories for commenting in male and female photos, respectively, more than other Table 1: Emoji preferences of Facebook users in commenting female and male-owned pictures with specific alt-text tags. Facebook users react more by Haha to male-owned photos, while they use more Love to react to female-owned photos. This outcome is additional evidence to the previous result which commits the Facebook users different emotional response to male and female posted pictures. To sum up, there is a significant disparity in using Emoji, based on the categories, and reaction when it comes to commenting, and reacting to female and male-owned pictures. These differences drive to gender inference attack. As such, the evaluations of the picture owner gender in forming others emotional responses, and the association of these responses with the content of the picture are two empirical questions that need to be investigated. To answer these questions, we apply Mutual Information (MI) in all three scenarios (See Section 1.3) to effectively conduct the differences, which measures (i) the mutual dependence between gender and received Emojis in the first scenario, (ii) the mutual dependence between picture owner gender and generated alt-text for that picture in the second scenario, and (iii) the correlation of picture owner gender with generated alt-text and received Emojis in the third scenario.

Let e and a denote the Emoji and alt-text tag respectively. Let X be a random variable that takes values x = 1 (the posted photo contain e in the first scenario, a in the second scenario, and a and e in the third scenario) and x = 0 (the posted photo does not contain e in the first scenario, a in the second scenario, and a and e in the third scenario), and Y is a random variable that shows the picture owner gender, where it takes values y = 1 for female and y = 0 for male. Note, there is no value for a in the first scenario, and e in the second scenario as we only consider the Emoji, and alt-text as the input data to our attack process respectively. Then, we compute the MI as follows: where P(X = x) ae , P(Y = y) ae are the marginal probabilities of x and y, and P(X = x, Y = y) ae is the joint probability of x and y. For example, the join probability of P(1, 1)

I (X ; Y ) ae = x ∈{0,1} y ∈{0,1} P (X = x , Y = y) ae loд 2 P (X = x , Y = y) ae P (X = x ) ae , P (Y = y) ae (1)
{1per son, }
is the probability that female-owned picture contains 1 person in the generated alt-text, and other Facebook users used for commenting for that picture. Additionally, we compute the probability of a person being male or female, given the picture generated alt-text, and commenting Emojis. For example, p(f emale |{1person, }) is the probability that the user is female if her picture contains 1 person in Facebook generated alt-text and received comments with Emoji from other Facebook users. Note, we compute the same probability for the first, and second scenario. For example, p(f emale | ) is the probability that the user is female if she received comments with Emoji from other Facebook users. Also, p(male |beard) shows the probability that the target user is male if the generated alt-text for his picture contains beard.

Table 2 illustrates the top 10 discriminative Emojis used by other Facebook users in commenting to female and male-owned pictures. Table 3 shows the discriminative alt-texts generated for female and male-owned pictures, while Tables 4, and 5 take into count the correlation of received Emojis and generated alt-text. Note that in Table 2, we draw inspiration from [START_REF] Chen | Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users[END_REF], and all entries in Table 2 are discriminative Emojis for female. From the same spirit, we show the top 10 discriminative alt-text, and correlation of alt-text and Emoji for female and male in table 3, 4, and 5.

FEATURES

In this section, we discuss our selected features and illustrate them with an example. Then we sketch the feature extraction algorithms.

Feature Selection

Feature selection is the process of identifying and selecting relevant features correlated to variables of interest (in our case gender). The purpose of feature selection is three-fold: promoting the model prediction performance, providing faster and efficient classifiers, and reducing the data dimensionality that decreases the complexity of the model. We extract features in four different categories, which consist of:

5.1.1 N-grams. To capture syntactic similarities, we employed ngrams on Facebook generated alt-text. N-grams are a set of cooccurring words within a given window size (n). Our previous experiments showed that introducing 6-grams on alt-texts degraded the performance, and we kept the lengths up to 5-grams for alt-texts dataset [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF]. In this work, we use our previous experimental result and apply 5-grams on the alt-text. Additionally, we run 1-grams on Emojis, as other n-grams do not perform best on Emoji. We keep

Table 6: Pattern-based features

Non-textual Single Emoji without text. For example, Figure 1(b) Repeated Emoji without text. For example, Figure 1(a) forth comments. Textual Single Emoji. For example, the third and fourth comments of Image 1.2.

Single repeated Emoji. For example, the last comment of Figure 1(a). Several repeated/non-repeated Emojis. For example, the first comment of Image 1.2.

the result of n-grams that appear more than 50 times in total. With that, we collected 894 features.

Patterns.

The different Emojis preferences of Facebook users in commenting on female or male pictures follow some patterns, from which we derive features. We divided these patterns into nontextual and textual categories. The non-textual category contains comments which have only Emoji (no words), where the textual category contains words and Emoji. Table 6, defines our five patternbased features. 1, Emoji preferences in commenting pictures are significantly different according to picture owner gender. We compute the frequency of each Emoji concerning all Emojis. To reduce the number of selected features, and the complexity, we consider only Emojis that Facebook users used more than 50 times in total. By considering that, we extracted 735 features. 4 and5 confirm the difference in alt-text and Emoji correlation for male and female posted pictures. These differences lead us to consider pairs of alttext and Emoji as features. To that end, we constructed the cooccurrence network for Emojis and alt-text. Note that we drop rare co-occurrence pairs that appear less than 50 times in all the pictures. Finally, we collected 1363 features from all the possible combination of Emojis, and alt-text in our data set.

Emoji Usage. As presented in Table

Correlation of alt-text and Emoji. Tables

In total, we selected 2992 features from the above different categories. After selecting these features, we needed to apply feature extraction algorithms to prune and reduce the selected features.

Feature Extraction

The goal of feature extraction is to downsample the selected features while keeping those that contribute more to predicting the final result (in our female and male). We examined four feature extraction methods (Chi-Square 12 Information Gain13 Feature importance 14 , and Univariate feature selection 15 ) in our previous work. We evaluated all the possible individual and combined feature extraction methods to achieve the best features set [START_REF] Alipour | Gender Inference for Facebook Picture Owners[END_REF]. In this study, we used our previous algorithm with a slight change. We separately run the feature extraction methods on A, E, and O data sets, and we did not merge the output features set of each data set. As a result, we only run the feature extraction methods one time.

Data Set

Using a Python crawler, we collected a set of 141,812 pictures and their 446,655 messages. Our statistic showed that 1291 different types of Emoji appear in our data set. We randomly selected Facebook users to not biased the data collection by region or country usage preferences. Facebook was unable to generate alt-text for 13000 pictures. We kept those pictures for our first attack scenario, where we purely rely on Emojis. Note that although most collected data set comprises textual content, we only considered the Emojis in our study.

Results

The experimental results are achieved by applying the classifiers from the Python library scikit-learn. We model gender inference attack as a binary classification problem. To achieve robust results, we apply several supervised machine learning algorithms such as Logistic Regression, Random Forests, K-Nearest Neighbors, Support Vector Machine, Naive Bayes and Decision Tree. To evaluate the classifier, we select the same number of male and female to prevent biased classification. Train-test splitting was preferable in this study as it runs k-times faster than k-fold. We vary the size of the training set to measure the difference in the attack accuracy. Finally, we choose the train-test size of 75-25, which gives the best accuracy. To address the problem of fairly estimating the performance of each classifier, and make sure the classifiers can generalize to unseen data, we split the training data set of train-test into training, and validation data set 75-25. We randomly select the training data set of train-validation and train the classifiers by using these data set. Later, we record the performance on the validation data set and adjust the hyper-parameters to optimize the performance of the classifiers. Eventually, we evaluate the classifiers on the test data set of train-test. Considering the extracted gender as the ground-truth, to evaluate our attack, we compute the standard Accuracy, Precision, Recall, and F1_score metrics. In Tables 7 and8, we report the results of the test data set for gender inference attack. In these two tables, we compare our six classifiers on three different scenarios.

In the first scenario, we purely conduct an inference attack by using other Facebook users Emoji preferences in commenting on the target user pictures. As we mention in Section 5.2, we use our previous feature selection algorithm to select the best features. The Feature importance method performs the best and generates 258 features. We evaluate the effectiveness of the selected features by training the classifiers on these features. Selecting the best machine learning algorithms based on evaluation metrics depends entirely on the problem. We consider the gender inference attack as a binary classification problem and since we are dealing with a balanced data set, we used Accuracy as important metrics to evaluate each classifier performance. As illustrated in Table 7 (left section), Logistic Regression classifier outperforms other classifiers in this scenario. According to the result, Logistic Regression can infer the target user gender with accuracy 76%. Logistic Regression is a discriminative model which is appropriate to conduct when the dependent variable is binary. So, it learns better than other classifiers between the dependent and independent variable in the first scenario. We can observe that Logistic Regression performs better than other supervised classifiers in the first scenario based on Accuracy, Recall and F1-Score. Logistic Regression also performs slightly worse than Support Vector Machine and Naive Bayes classifiers in Precision. The result confirms the preference Emojis usage of other Facebook users is enough to implement a machine learning classifier to infer the target user gender.

In the second scenario, we rely on Facebook generated alt-text to infer the picture owner gender. Table 7 (right section) displays our inference results on the extracted alt-texts features. The intersection of Feature importance and Chi-Square perform the best and generate 316 features. To measure the efficiency of the features, we train classifiers by using only these features. According to Table 7 (right section), Support Vector Machine performs better than Logistic Regression in Accuracy. Based on the result, an attacker can infer the target user gender with an accuracy of 80%. we can observe that Logistic Regression performs slightly better than Support Vector Machine in Precision and Recal. Moreover, Support Vector Machine also perform slightly worse than Logistic Regression and Naive Based in F1-Score. Support Vector Machine is a discriminative classifier defined by a separating hyperplane. The goal of the algorithm is to determine a hyperplane in N-dimensional space where N is the number of features, in our case 316, that precisely classify the new example. In the second scenario, SVM outputs an optimal hyperplane that classifies female from male better than other classifiers.

In the third scenario, we train the machine learning classifiers by using the co-occurrence of alt-texts and Emoji preferences of other Facebook users. We show the result of each classifier for the General rule, and Restricted rule separately. The intersection of Feature importance and Univariate feature selection performs the best and creates 224, and 211 features for General rule, and Restricted rule, respectively. Table 8 (left section) displays our inference attack results by considering the General rule. It shows that the Logistic Regression performs better than other classifiers in Accuracy, Recall, and F1-Score when using these 224 features. Naive Bayes classifier received a slightly higher Precision score than Logistic Regression. The Logistic Regression model, which had 76% accuracy in the first scenario, and 80% in the second scenario gets a 7%, and 3% accuracy boost in this scenario, which is a fairly substantial gain in accuracy. On the other hand, Table 8 (right section) shows the result of the Restricted rule. As illustrated, the accuracy increased 4% in compare to General rule. Similar to the General rule result, Logistic Regression performs better than other classifiers in Accuracy, Recall, and F1-Score. Naive Bayes, and Support Vector Machine perform best in Precision. Note, by setting the Restricted rule, we filter 30,595 pictures more than the General rule. The results in Table 8 shows the effect of third scenario in increasing attack accuracy.

To conclude, Logistic Regression performs best in first and third scenarios, and Support Vector Machine was the suitable classifier for the second scenario. The results confirm our hypothesis that the gender and contents of the picture have an impact on Facebook users emotional responses. Moreover, there is a substantial difference in receiving Emojis between female and male posted pictures on Facebook. As a result, an attacker can train standard classifiers by using non-user generated data (Emoji preferences of other Facebook users and generated alt-text) to infer the picture owner gender. Note, as we relied solely on non-user generated data, the results cannot be compared to previous works that used user-generated data.

DISCUSSION

Based on our analysis, the best scenario for the attacker is the third scenario, Restricted rule, when he has access to Facebook generated alt-text and other Facebook users posted Emojis. Selecting General rule and Restricted rule depends on the quantity of the pictures, after setting filtering rules. Initially, the attacker applies the Restricted rule, and verify if the number of filtered images are suitable to continue the attack. Otherwise, the attacker picks the General rule to increase the number of pictures in his analysis. The attacker uses the General rule if the number of crawled images after filtering pictures are not sufficient to be analyzed. The second scenario is suitable when the crawled pictures contain only generated alt-text and no Emoji(s) commented by target friends, friends of friends, or ordinary users. In this case, the generated alt-text can help the attacker to infer the target user gender. The first scenario is useful when the Facebook algorithm, in rare cases, is enabled to generate alt-text for the target user pictures. In this case, the attacker has the advantage of using Emojis, the universal language, and launch the gender inference attack. A limitation of our work concerns the type of data that we collect. As we mentioned earlier, we only collect non-user generated data (picture meta-data). However, this data set might not be suitable for another social network such as Twitter. For example, Facebook generates a description of posted pictures (alt-text), which is not yet accessible on Twitter. As a result, it disables a more comprehensive analysis of other social media. Additionally, as the meaning of Emojis may differ on each platform, we are not sure that our analysis remains valid for Emoji usage by Twitter users.

Identifying users gender from their online activities and data sharing behavior is an important topic in the growing research field of social networks. It provides an opportunity for targeted advertising, profile customization, or privacy risks. This study has investigated 141,812 images and their 446,655 comments. Based on the intensive analyses of the shared images, this work has demonstrated a new perspective of gender inference attack on Facebook users by relying on non-user generated data. We have shown the possibility of gender inference attack even when all user attributes/activities are hidden, such as profile attributes, friend list, liked pages and joined groups. Our experimental results showed that on average female posted pictures receive more Emojis-based than male posted pictures. Additionally, we have shown alt-text gives extra free information that boosts inference accuracy. We have noticed that other Facebook users use more emotional Emojis to comment on female posted images with a particular theme and setting.

As future work, we plan to (i) apply sentiment analysis and consider the meaning of Emojis/Emoticons in the classification task, (ii) compare Deep Learning approaches with current Machine Learning algorithms, (iii) create an application to deal with online gender inference attacks, and (iv) propose counter-measures to picture owners, considering a trade-off between privacy risks and comments-based social benefits.

Figure 1 :

 1 Target user received comments: (a) Emojis and non-English words (b) only Emojis.

Image 1 :

 1 Generated alt-text: 1 person, child, sleeping and bedroom Comment: Precious!!! Comment: Priceless moments! Love y'all Comment: I love this!!!

  illustrated in Figure2(a), Facebook users use more emotional Emojis from Symbol category, which contains heartbased Emojis, to express their feeling to female-owned images. While, they have a higher tendency in using Emojis from Smileys category, which holds face-based Emojis, to comment to male-owned pictures. 4.2.2 Reactions Modes. Following the previous findings, we go further and plot Facebook users reaction to the observed pictures. In general, there are six different reactions on Facebook (as it is shown in Figure 2(b)). The result shows that Facebook users Like male and female posted images frequently, in comparison to other reaction types. Although this reaction is very close, female pictures received slightly more Like than male pictures. According to Figure 2(b),

Figure 2 :

 2 Figure 2: Received emotional responses: (a) Emojis based on categories (b) Reactions.

Table 2 :

 2 Discriminative Emojis for female and male-owned picture

	Emoji	MI	p(female|Emoji) p(male|Emoji)
		0.026	0.84	0.16
		0.025	0.89	0.11
		0.015	0.86	0.14
		0.011	0.91	0.09
		0.008	0.87	0.13
		0.008	0.92	0.08
		0.006	0.82	0.18
		0.006	0.92	0.08
		0.005	0.91	0.09
		0.005	0.90	0.10

Table 3 :

 3 Discriminative alt-text for female and male-owned picture

	alt_text	MI	p(female|alt_text) p(male|alt_text)
	closeup	0.015	0.80	0.20
	smiling	0.011	0.75	0.25
	1 person	0.006	0.70	0.30
	1 person smiling 0.006	0.80	0.20
	smiling closeup 0.006	0.89	0.11
	1 person closeup 0.004	0.85	0.15
	beard	0.004	0.28	0.72
	2 people smiling 0.004	0.74	0.26
	car	0.003	0.28	0.72
	selfie closeup	0.003	0.82	0.18

Table 4 :

 4 Discriminative Emojis and alt-text for femaleowned picture (Restricted rule)

	Emoji + alt_text	MI	p(f emale |Emoji) p(male |Emoji)
	(1 person, )	0.044	0.89	0.11
	(1 person, )	0.036	0.87	0.13
	(closeup, )	0.027	0.93	0.07
	(closeup, )	0.021	0.92	0.08
	(1 person, )	0.020	0.85	0.15
	(1 person, )	0.019	0.86	0.14
	(1 person, )	0.017	0.93	0.07
	(smiling, )	0.016	0.93	0.07
	(closeup, )	0.015	0.92	0.08
	(smiling, )	0.013	0.90	0.10

Table 5 :

 5

	Emoji + alt_text	MI	p(female|Emoji) p(male|Emoji)
	(beard, )	0.009	0.09	0.91
	(beard, )	0.003	0.17	0.83
	(beard, )	0.002	0.11	0.89
	(hat, )	0.002	0.22	0.78
	(beard,	0.002	0.08	0.92
	(outdoor, )	0.002	0.22	0.78
	(outdoor, )	0.002	0.37	0.63
	(smiling, )	0.001	0.23	0.77
	(sky, )	0.001	0.40	0.60
	(standing, )	0.001	0.33	0.67

Discriminative Emojis and alt-text for male-owned picture (Restricted rule)

Table 7 :

 7 Machine Learning classifiers performance with optimal hyper-parameters.

			General Rule		Restericted rule
			Emojis + alt_text		Emojis + alt_text
		Accuracy Precision Recall Fscore	Accuracy Precision Recall Fscore
	LR	83.89	86.80 80.12 83.31	87.05	85.68 79.64	82.55
	KNN	77.69	83.48 72.49 77.59	76.79	83.67 70.17	76.32
	SVM	81.36	85.92 77.79 81.65	81.09	86.34 76.68	81.21
	NB	79.29	86.90 72.00 78.75	78.55	86.82 70.45	77.78
	DT	69.39	72.84 68.07 70.32	72.13	75.29 71.14	73.11
	RF	76.72	81.08 73.48 77.09	76.80	82.17 72.16	76.83

Table 8 :

 8 Machine Learning classifiers performance with optimal hyper-parameters.

https://www.aclu.org/blog/womens-rights/womens-rights-workplace/ facebook-settles-civil-rights-cases-making-sweeping

https://languages.oup.com/press/news/2019/7/5/WOTY

https://en.wikipedia.org/wiki/Emoticon

https://en.wikipedia.org/wiki/List_of_Emoticons

https://www.csh.rit.edu/~kenny/misc/smiley.html

https://www.urbandictionary.com/

http://Emojicodes.com/

http://www.Emoticonr.com/

http://sheet.shiar.nl/emoji

https://getemoji.com/

https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2. html

https://www.bogotobogo.com/python/scikit-learn/scikt_machine_learning_ Decision_Tree_Learning_Informatioin_Gain_IG_Impurity_Entropy_Gini_ Classification_Error.php

https://www.scikit-yb.org/en/latest/api/features/importances.html

https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_ selection.html6 EXPERIMENTSIn this section, we demonstrate, and evaluate the experimental result of our approach for all three scenarios.

.fr/fr/article/digitrust/).


Amaury Kasprowiak

Francine Cazier-Dennin

Pierre-Edouard Danjou
email: danjou@univ-littoral.fr

Flash chromatography system: a practical tool for demonstrating the influence of column characteristics on chromatographic resolution

A flash purification system using prepacked cartridges containing different silica weight and particle sizes was employed to demonstrate the influence of column characteristics on chromatographic resolution. This chromatographic experiment was a helpful introduction to chromatographic courses where the influence of particle sizes was addressed only theoretically and it allows students to experience the concept in an active learning style experiment. After running a predefined set of experiments to understand concepts, students were asked to design their own experiment to achieve the best chromatographic resolution. Moreover, the difference of mass versus concentration detectors was highlighted using Ultraviolet (UV) and Evaporative Light Scattering Detectors (ELSD). Questionnaires were also used to define the students' initial knowledge and to assess on what they learned following this lab.

GRAPHICAL ABSTRACT KEYWORDS

Chromatography, Analytical Chemistry, Laboratory Instruction, Hands-On Learning/Manipulatives, Upper-Division Undergraduate, Laboratory Equipment / Apparatus The concept of flash chromatography was first introduced in 1978 with the promise of separating several grams of compound in a very short time [START_REF] Still | Rapid Chromatographic Technique for Preparative Separations with Moderate Resolution[END_REF] (15 minutes instead of 2-3 hours). While this technique is commonly used by organic chemists, it has not undergone any major evolution over several decades and remains the method of choice for the purification of organic compounds. Some minor modifications were proposed to facilitate access to this technique in teaching laboratories [START_REF] Jacobson | An Inexpensive Way to Do Flash Chromatography[END_REF][START_REF] Pontén | Safe and Efficient Flash Chromatography Equipment for the Research/Teaching Lab[END_REF][START_REF] Butler | Flash Chromatography: A Novel Pressurization Apparatus[END_REF] .

However, the emergence of automated flash chromatography systems like Reveleris (Buchi) [START_REF]Buchi English-language homepage[END_REF] , Isolera (Biotage) [START_REF]Biotage English-language homepage[END_REF] or Puriflash (Interchim) [START_REF]Interchim English-language homepage[END_REF] , has deeply changed the ways compounds are purified and open new teaching opportunities in the organic synthesis as well as in analytical chemistry. It is obvious that such devices will find their usefulness in the organic chemistry laboratory in complement/replacement of traditional column chromatography [START_REF] Naumiec | A Modern Apparatus for Performing Flash Chromatography: An Experiment for the Organic Laboratory[END_REF] since it became increasingly popular in companies as well as in academic research labs. Moreover, the use of prepacked silica cartridges prevents the handling of hazardous silica gel [START_REF] Husgafvel-Pursiainen | Mutations, tissue accumulations, and serum levels of p53 in patients with occupational cancers from asbestos and silica exposure[END_REF] by the students, leading to a safer experiment.

Nevertheless, it could also be interesting to introduce flash chromatography systems in a chromatography module as it gives access to a large variety of prepacked cartridges of different sorbent weights, particles diameters, and phase nature (normal, reverse, grafted, chiral phases etc.).

Sorbent weights could easily be related to HPLC column length and/or diameter since in both cases it presents an increase in active site number. It also gives the students notions regarding HPLC column choice. This variety of cartridges allows the study of other parameters than those commonly studied in typical liquid chromatography labs that are mainly focused on the influence of mobile phase The influence of some parameters like silica weight (i.e. column loading), particle sizes 10 , type of detectors [START_REF] Swartz | HPLC detectors: a brief review[END_REF] is often taught theoretically but students are rarely offered the possibility to demonstrate their influences during a lab session. This lack of practical experience is due to the fact that it is not common to own traditional HPLC columns in different lengths and silica particle sizes. Moreover, most of the time, HPLC only have one detector ranging from the most common UV-visible detector [START_REF]Interchim English-language homepage[END_REF][START_REF] Miranda | Titration and HPLC Characterization of Kombucha Fermentation: A Laboratory Experiment in Food Analysis[END_REF][START_REF] Purcell | Extraction and Antibacterial Properties Of Thyme Leaf Extracts: Authentic Practice of Green Chemistry[END_REF] to the most advanced (and expensive) tandem mass spectrometer. [START_REF] Parker | What Is in Your Wallet? Quantitation of Drugs of Abuse on Paper Currency with a Rapid LC-MS/MS Method[END_REF][START_REF] Betts | Teaching Undergraduates LC-MS/MS Theory and Operation via Multiple Reaction Monitoring (MRM) Method Development[END_REF] To address this lack of practical demonstration, we have designed a laboratory experiment (Figure 1) in which students are invited to experience different chromatographic parameters in an active learning style experiment thanks to an automated flash chromatography system. To the best of our knowledge, it is the first time that a flash chromatography device is used for teaching purposes.

LEARNING OUTCOMES

At the end of the lab session, students are able to:

(1) Explain the operation of a flash chromatography system

(2) Separate a mixture of compounds using flash chromatography system

(3) Explain the influence of sorbent weight as well as silica particle sizes on chromatographic resolution (4) Conduct a scale change using Column Volume (CV)

(5) Predict elution order on normal phase chromatography and compare with reverse phase HPLC (6) Explain the difference between mass and concentration detector 16

EXPERIMENTAL PROCEDURE

Apparatus description

The flash chromatography device used in this study is a classical commercial apparatus incorporating a quaternary gradient pump system, diode array detector (200 -600 nm) allowing to monitor up to 4 wavelengths simultaneously and a full UV spectrum as well as an Evaporative Light Scattering Detector (ELSD) for universal detection.

Column choice

Gradient optimization was performed on prepacked silica cartridges loaded with 25 g of 50 µm silica particles. Using the method of Column Volumes (CV), the gradient was transferred to cartridges with different dimensions and sorbent loadings (i.e., 12, 25 and 40 g). The same gradient was applied on the 15 μm silica particles at a 25 g loading. Cartridges were reused several times (more than 5 times) during the same day without efficiency loss. As stated by the supplier and experienced by us, shelf-life of a column under hexane does not exceed one day.

Stock phthalate solution

Using a volumetric flask and ethyl acetate (analytical grade), students prepared 10 mL of stock solution containing 2g of each dialkyl phthalates: dimethylphthalate (DMP, >99%), diethylphthalate (DEP, 99.5%), dipropylphthalate (DPP, 98%), dibuthylphthalate (DBP, 99%) This solution was used for each experiment.

Journal of Chemical Education 10/21/20 Page 5 of 11

Elution conditions

To avoid bias in comparison between column, students were asked to apply the following gradient (Table 1) expressed in Column Volume (CV) instead of time. During optimization, it was found that an adequate but not perfect resolution could be achieved in about 8 CV. [START_REF] Mariana | The effects of phthalates in the cardiovascular and reproductive systems: A review[END_REF] and should be manipulated with care.

RESULTS

The first set of experiments was performed using cartridges containing 50 µm spherical silica in order to demonstrate the influence of column loading on the resolution (Table 2, entry 1-3). Gradient, injection volume and concentration of phthalates in solution remained constant for all experiments.

The optimal flow rate for 40 g cartridge was chosen according to manufacturer data (entry 3) instead of the same 15 mL/min flow for all cartridges since no differences were evidenced between 26 and 15 mL/min (see supplementary data Afterwards, an experiment was run using 25g of 15 µm spherical silica in order to visualize the effect of particle sizes diminution on the chromatographic resolution. Erreur ! Source du renvoi introuvable., entry 2 and 4 experiments were presented Figure 3. Students can then easily notice a better resolution (1.13) between DBP and DPP on this cartridge (25 g ,15µm silica), similar to the one obtained with 40 g cartridge (50 µm). At this point, the students were able to understand that if the CV remains fairly constant for all the compounds, the volume of solvent used is 40% lower on the cartridge containing 25 g of silica (260 mL against 416 mL) leading to a more practical and economical separation with smaller silica particles. A comparison of responses between the UV-detector (254 nm) and ELSD recorded during run 4 from table 2 was presented in Figure 4. It can be observed that the UV-detector provides a similar intensity of response for the four phthalates analytes while a great disparity was observed with ELSD leading to a poor signal for the smallest compound (DMP). This can be explained by the fact that in UV detection the signal is proportional to the analyte's concentration according to the Beer-Lambert's law while ELSD is sensitive to molecules sizes after solvents' evaporation, leading to, in general, intense signal for larger compounds. chromatograph. Those differences in student's knowledge explain why the chromatography program in our formation starts from scratch and why we choose to perform this module with graduate students and not undergraduate students. We believe that undergraduate courses dealing with chromatography theory could also benefit from this experiment.

The pedagogical staff decided to offer this lab session to students at the beginning of the chromatography courses in order to promote discovery learning of some chromatographic parameters that will be taught later in the semester. It enriched the students' curricula by incorporating notions in normal phase chromatography, flash chromatography and ELSD detector. Students were asked to answer the same questionnaire (see supplementary data) before and after the lab session to evaluate their initial knowledge and learning. It is important to mention that the first questionnaire was completed while students didn't yet receive instructions for the lab session in order to truly evaluate their initial knowledge. A compilation of their answers was presented in table 3. Before the lab session, 60% of students never performed HPLC (Table 3 entry 1) and only 45% of students were able to mention UV as HPLC detector. The student who proposed the conductimetry was certainly confused with ion chromatography. The students probably guessed from the question that particle size affects the chromatogram (entry 3) but only 10% can explain how. No students were aware of the difference between mass and concentration detectors nor the existence or principle of the ELSD (entry 2). However, most of the students knew that the abscissa value on a chromatogram was the time (entry 6).

At the end of the lab session, all the students were able to mention in the questionnaire i) the type of detectors they employed (UV and ELSD) and if it was a mass or concentration detector ii) that particle sizes influence the chromatogram and how iii) that the abscissa value on a chromatogram could be expressed in time or Column Volume. Those results allow us to conclude that all the learning outcomes previously described were reached. A parallel was done during lecture between students' skill acquired during this lab session and the advantages of particle sizes diminution in UPLC as well as the difference between mass and concentration detectors. This parallel allows students to better understand those parameters that may seem abstract at first glance.

During the lab session students were asked to find a way to prove the elution order using common techniques. Among the responses, students proposed the following solutions: iii) Inject less solution (i.e. 0.5 mL instead of 1 mL) Among those solutions, the best resolutions of 1.50 and 1.73 between DBP and DPP were obtained by injecting 0.5 mL of the phthalates solution on cartridge containing 15 µm silica particle sizes with a silica weight of 25 g and 40 g respectively.

CONCLUSION

In conclusion, students were successfully introduced to automated flash chromatography, normal phase chromatography and the ELSD detector. They were given the opportunity to experience in an active learning style experiment the influence of column characteristics on chromatographic resolution as well as the different detectors responses on the same sample during the same chromatographic run. This lab experiment allows for a better understanding of i) column loading ii) particle sizes influence iii) mass vs concentration detector sensitivity.

ASSOCIATED CONTENT

Supporting Information

Instructions for students; Additional information for instructors (DOCX)

  composition on chromatographic separation. Moreover, some flash chromatography systems possess two different detectors, namely Ultraviolet (UV) and Evaporative Light Scattering (ELSD) Detectors, allowing students to compare the influence of the detection mode on chromatograms acquired during the same run.

Figure 1 :

 1 Figure 1 : Flow diagram of the lab session

  ). All chromatograms were expressed in Column Volume (CV) as it allows to compare data recorded on cartridges of different sorbent weights. To be precise, retention times are dependent on flow rate and sorbent weight while CV are not affected by such parameters and stay relatively stable. Another advantage of the use of CV is that it makes it easy to conduct a change of scale, either up or down, in flash chromatography. From Figure2, it can be seen that 12 g of 50 µm silica led to a poor separation of all phthalates, particularly DBP and DPP which are clearly coeluted. Increasing silica weight to 25 g allows us to discern the four different analytes with a resolution of only 0.76 for the first two peaks (DBP and BPP). This gain in resolution can be related to the Journal of Chemical Education 10/21/20 Page 6 of 11 increase in number of theorical plate which accompany increase in column length. As expected, a far better resolution (1.08) was obtained using a 40 g silica cartridge (entry 3) which results in a decrease in column loading. Resulting chromatograms were presented on Figure 2.

Figure 2 :

 2 Figure 2 : Separation of phthalates using different silica sorbent weights with 50 μm silica particles. A) 12 g, B) 25 g, and C) 40 g. Conditions: Cartridges and flow rates shown in Table 2, ambient temperature, 1 mL injection, 254 nm detection. Elution order: DBP, DPP, DET, DMP.

Figure 3 :

 3 Figure 3 : Separation of phthalates using different silica particles sizes at constant sorbent weight (25g). A) 50 μm, B) 15 μm. Conditions: 21 x 130 mm column, 15 mL/min flow, ambient temperature, 1 mL injection, 254 nm detection. Elution order: DBP, DPP, DET, DMP.

18

 18

Figure 4 : 11 DISCUSSION

 411 Figure 4 : Response of detectors during the separation of phthalates. A) UV at 254 nm, B) ELSD. Conditions: 21 x 130 mm column, 25 g silica, 15µm particle sizes, 15 mL/min flow, ambient temperature, 1 mL injection, Elution order: DBP, DPP, DET, DMP.

  i)Inject standards compounds one by one ii) Add more weight of a particular phthalate in the existing solution (standard addition)iii) Collect pure fraction from the column and evaporate the solvent under reduced pressure to obtained pure compound that can be analyzed by infrared spectroscopy, NMR spectroscopy, refractive index or mass spectrometry.Students were also asked how they can improve the chromatographic separation of the same mixture and to experiment with their proposal. The following solutions were proposed by students and tested during the lab session (see supplementary data):i) Increase sorbent weight with 15µm silica particles (i.e. 40 g silica cartridge with 15 µm particle sizes) the gradient (i.e. Ethyl acetate gradient in 8 CV instead of 5 CV)

Table 1 . Elution conditions

 1

	CV	n-hexane	Ethyl acetate
	0	100	0
	5	80	20
	8	80	20
	HAZARDS
	Since the experiment requires manipulation of organic solvents and compounds as well as a

potentially high-pressure device, students must wear appropriate protective equipment i.e. lab coat, goggles, and gloves. Ethyl acetate and n-hexane are highly inflammable, irritant liquids, and should be manipulated with care in a well-ventilated fume hood. n-hexane is neurotoxic, suspected of damaging fertility, and toxic to aquatic life. Solvent should be disposed properly. Phthalates are potentially toxic for human reproduction

Table 2 . Experiment Overview

 2

	Entry		Cartridge Conditions	CV0 ,	Optimal	Column		Resolutions c
		Weight, g Sorbent	µm Particle Size,	 Length, mm Internal Diameter	mL a	Flow, mL/min	Loading, % b	DBP/DPP DPP/DEP DEP/MP
	1	12	50	21  78	20.5	15	6.6	ND d	ND d	1.16
	2	25	50	21  127	32.5	15	3.2	0.76	1.41	1.92
	3	40	50	27  127	52.0	26	2.0	1.08	1.85	2.35
	4	25	15	21  127	32.5	15	3.2	1.13	2.68	2.95

a Column Volume according to manufacturer data. b Weight of analyte/weight of silica. c Resolutions were determined graphically. d Not determined due to DBP and DPP coelution.

Table 3 . Distribution of Students' Responses to the Pre-Lab Questionnaire

 3

	Item Questions for Response	Student Responses, N = 20 Remarks
			Yes, % No, % No Answer, %
	1	Had you ever used a liquid chromatograph?	60	40	0
	2	Had you ever heard of ELSD?	5	95	0	None could explain
	3	Does the size of the silica particles in a column	50	0	50	Only 10% could explain
		affect the chromatogram?
	4	Before the lab had you ever heard of flash	40	60	0	Only 30% could explain
		chromatography?
	5	What type (s) of detector (s) did you use?	45 a	5 b	50
	6	What is the magnitude measured on the abscissa?	70 c	5 d	20

a Percentage of students responding with "UV". b Percentage of students responding with "Conductimetry". c Percentage of students responding with "Time". d Percentage of students responding with "Absorption".

ACKNOWLEDGMENTS

Authors would like to thanks the 2018-19 and 2019-20 promotions of the "Analyse Chimique, Contrôle Industriel, Environnement" master for testing the protocol, enriching the reproducibility data and their critical point of view on this work. Authors are also grateful to FCU Côte d'Opale and Université du Littoral Côte d'Opale (ULCO) for financial support. Journal of Chemical Education 10/21/20

AUTHOR INFORMATION

Corresponding Author *E-mail: danjou@univ-littoral.fr